text:
the original CT images was created. For 10 prostate cancer patients, the dose difference between the reference dose distribution and inverse distorted CT dose distribution was analyzed in isodose level bins. The mean magnitude of the geometric distortion was 1.97 mm for the radial distance of 200-250 mm from isocenter. The mean percentage dose differences for all isodose level bins, were 0.02{\%} and the radiotherapy structure mean volume deviations were {\textless}0.2{\%}. The method developed can quantify the dosimetric effects of MRI system specific distortion in a prostate MRI only radiotherapy workflow, separated from dosimetric effects originating from synthetic CT generation. No clinically relevant dose difference or structure deformation was found when 3D distortion correction and high acquisition bandwidth was used. The method could be used for any MRI sequence together with any anatomy of interest.},
author = {Gustafsson, C. and Nordstr{\"{o}}m, F. and Persson, E. and Brynolfsson, J. and Olsson, L. E.},
doi = {10.1088/1361-6560/aa5fa2},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {MRI only,MRI radiotherapy,MRI treatment planning,QA,distortion,synthetic CT},
number = {8},
pages = {2976--2989},
title = {{Assessment of dosimetric impact of system specific geometric distortion in an MRI only based radiotherapy workflow for prostate}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {62},
year = {2017}
}
@article{Hajdu2006,
abstract = {In this paper we present the structure and the achieved results of the R{\&}D project IKTA-4, 6/2001 "MEDIP - Platform independent software system for medical image processing" supported by the Hungarian Ministry of Education. The aim of the project was to develop a software background for our basic and applied research in the field of medical imaging that can be used in clinical routine, as well. Realization was based on the experience of information technology and medical imaging research university teams and a company specialized on software and hardware developing for nuclear medicine. The aims also reflect some former research and development activities of the participants. Thus some of them are well experienced in registration, segmentation and image fusion techniques. These experiences were also considered in the determination of the main purposes. The capabilities of the provided software library were demonstrated through test applications from the fields of orthopedics, oncology and nuclear medicine. {\textcopyright} J.UCS.},
author = {Hajdu, Andr{\'{a}}s and Kormos, J{\'{a}}nos and Lencse, Zsolt and Tr{\'{o}}n, Lajos and Emri, Mikl{\'{o}}s},
doi = {10.3217/jucs-012-09-1229},
issn = {0958695X},
journal = {Journal of Universal Computer Science},
keywords = {Medical image processing,Multimodal image analysis,Surface rendering,Virtual surgery,Visualization,Volume rendering},
number = {9},
pages = {1229--1239},
title = {{The "MEDIP - Platform independent software system for medical image processing" project}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {12},
year = {2006}
}
@inproceedings{Hamarneh,
abstract = {Recent advances in medicine conjecture that certain body fat may have mechanical function in addition to its classical role of energy storage. In particular we aim to analyze if the intra-articular fat pad of Hoffa is merely a space holder or if it changes shape to provide cushioning for the knee bones. Towards this goal, 3D CT images of real knees, as well as a skeletal knee model with fat simulating Hoffa's pad, were acquired in both extension and flexion. Image segmentation was performed to automatically extract the real and simulated fat regions from the extension and flexion images. Utilizing the segmentation results as binary masks, we performed automatic multi-resolution image registration of the fat pad between flexed and extended knee positions. The resulting displacement fields from flexion-extension registration are examined and used to calculate local fat volume changes thus providing insight into shape changes that may have a mechanical component.},
author = {Hamarneh, Ghassan and Chu, Vincent and Bordalo-Rodrigues, Marcelo and Schweitzer, Mark},
booktitle = {Medical Imaging 2005: Physiology, Function, and Structure from Medical Images},
doi = {10.1117/12.594803},
issn = {16057422},
pages = {527},
title = {{Deformation analysis of Hoffa's fat pad from CT images of knee flexion and extension}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-23844492895{\&}doi=10.1117{\%}2F12.594803{\&}partnerID=40{\&}md5=5a48120f6f2668def17638f909192147},
volume = {5746},
year = {2005}
}
@article{Han2018,
abstract = {Neuro-navigated procedures require a high degree of geometric accuracy but are subject to geometric error from complex deformation in the deep brain - e.g. regions about the ventricles due to egress of cerebrospinal fluid (CSF) upon neuroendoscopic approach or placement of a ventricular shunt. We report a multi-modality, diffeomorphic, deformable registration method using momentum-based acceleration of the Demons algorithm to solve the transformation relating preoperative MRI and intraoperative CT as a basis for high-precision guidance. The registration method (pMI-Demons) extends the mono-modality, diffeomorphic form of the Demons algorithm to multi-modality registration using pointwise mutual information (pMI) as a similarity metric. The method incorporates a preprocessing step to nonlinearly stretch CT image values and incorporates a momentum-based approach to accelerate convergence. Registration performance was evaluated in phantom and patient images: first, the sensitivity of performance to algorithm parameter selection (including update and displacement field smoothing, histogram stretch, and the momentum term) was analyzed in a phantom study over a range of simulated deformations; and second, the algorithm was applied to registration of MR and CT images for four patients undergoing minimally invasive neurosurgery. Performance was compared to two previously reported methods (free-form deformation using mutual information (MI-FFD) and symmetric normalization using mutual information (MI-SyN)) in terms of target registration error (TRE), Jacobian determinant (J), and runtime. The phantom study identified optimal or nominal settings of algorithm parameters for translation to clinical studies. In the phantom study, the pMI-Demons method achieved comparable registration accuracy to the reference methods and strongly reduced outliers in TRE (p 0.001 in Kolmogorov-Smirnov test). Similarly, in the clinical study: median TRE = 1.54 mm (0.83-1.66 mm interquartile range, IQR) for pMI-Demons compared to 1.40 mm (1.02-1.67 mm IQR) for MI-FFD and 1.64 mm (0.90-1.92 mm IQR) for MI-SyN. The pMI-Demons and MI-SyN methods yielded diffeomorphic transformations (J {\textgreater} 0) that preserved topology, whereas MI-FFD yielded unrealistic (J {\textless} 0) deformations subject to tissue folding and tearing. Momentum-based acceleration gave a ∼35{\%} speedup of the pMI-Demons method, providing registration runtime of 10.5 min (reduced to 2.2 min on GPU), compared to 15.5 min for MI-FFD and 34.7 min for MI-SyN. The pMI-Demons method achieved registration accuracy comparable to MI-FFD and MI-SyN, maintained diffeomorphic transformation similar to MI-SyN, and accelerated runtime in a manner that facilitates translation to image-guided neurosurgery.},
author = {Han, R. and {De Silva}, T. and Ketcha, M. and Uneri, A. and Siewerdsen, J. H.},
doi = {10.1088/1361-6560/aae66c},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {deformable registration,image registration,neurosurgery navigation},
number = {21},
pages = {18},
pmid = {30353886},
title = {{A momentum-based diffeomorphic demons framework for deformable MR-CT image registration}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {63},
year = {2018}
}
@article{He2018,
abstract = {Exercise preconditioning induces neuroprotective effects during cerebral ischemia and reperfusion, which involves the recovery of cerebral blood flow (CBF). Mechanisms underlying the neuroprotective effects of reestablished CBF following ischemia and reperfusion are unclear. The present study investigated CBF in hyper-early stage of reperfusion by laser speckle contrast imaging, a full-field high-resolution optical imaging technique. Rats with or without treadmill training were subjected to middle cerebral artery occlusion followed by reperfusion. CBF in arteries, veins, and capillaries in hyper-early stage of reperfusion (1, 2, and 3 h after reperfusion) and in subacute stage (24 h after reperfusion) were measured. Neurological scoring and 2,3,5-triphenyltetrazolium chloride staining were further applied to determine the neuroprotective effects of exercise preconditioning. In hyper-early stage of reperfusion, CBF in the rats with exercise preconditioning was reduced significantly in arteries and veins, respectively, compared to rats with no exercise preconditioning. Capillary CBF remained stable in the hyper-early stage of reperfusion, though it increased significantly 24 h after reperfusion in the rats with exercise preconditioning. As a neuroprotective strategy, exercise preconditioning reduced the blood perfusion of arteries and veins in the hyper-early stage of reperfusion, which indicated intervention-induced neuroprotective hypoperfusion after reperfusion onset.},
author = {He, Zhijie and Lu, Hongyang and Yang, Xiaojiao and Zhang, Li and Wu, Yi and Niu, Wenxiu and Ding, Li and Wang, Guili and Tong, Shanbao and Jia, Jie},
doi = {10.1109/TBME.2017.2695229},
issn = {15582531},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {Cerebral blood flow,Cerebral ischemia,Exercise preconditioning,Laser speckle contrast imaging,Reperfusion injury},
number = {1},
pages = {219--223},
title = {{Hypoperfusion induced by preconditioning treadmill training in hyper-early reperfusion after cerebral ischemia: A laser speckle imaging study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {65},
year = {2018}
}
@article{Herrmann2016,
abstract = {One of the most important factors to determine the mechanical properties of a fibre composite material is the orientation of the fibres in the matrix. This paper presents Hessian matrix-based algorithms to retrieve the orientation of individual fibres out of steel fibre reinforced cementitious composites samples scanned with an X-ray computed tomography scanner. The software implemented with the algorithms includes a massive data filtering component to remove noise from the data-sets and prepare them correctly for the analysis. Due to its short computational times and limited need for user intervention, the software is able to process and analyse large batches of data in short periods and provide results in a variety of visual and numerical formats. The application and comparison of these algorithms lead to further insight into the material behaviour. In contrast to the usual assumption that the fibres act only along their main axis, it is shown that the contribution of hooked-end fibres in other directions may be noticeable. This means that fibres, depending on their shape, should act as orthotropic inclusions. The methods can be used by research laboratories and companies on an everyday basis to obtain fibre orientations from samples, which in turn can be used in research, to study stress–strain behaviour, as input to constitutive models or for quality assurance.},
author = {Herrmann, Heiko and Pastorelli, Emiliano and Kallonen, Aki and Suuronen, Jussi Petteri},
doi = {10.1007/s10853-015-9695-4},
issn = {15734803},
journal = {Journal of Materials Science},
number = {8},
pages = {3772--3783},
title = {{Methods for fibre orientation analysis of X-ray tomography images of steel fibre reinforced concrete (SFRC)}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {51},
year = {2016}
}
@article{Heyde2013,
abstract = {Although real-time 3-D echocardiography has the potential to allow more accurate assessment of global and regional ventricular dynamics compared with more traditional 2-D ultrasound examinations, it still requires rigorous testing and validation should it break through as a standard examination in routine clinical practice. However, only a limited number of studies have validated 3-D strain algorithms in an invivo experimental setting. The aim of the present study, therefore, was to validate a registration-based strain estimation methodology in an animal model. Volumetric images were acquired in 14 open-chest sheep instrumented with ultrasonic microcrystals. Radial strain (e{\{}open{\}}RR), longitudinal strain (e{\{}open{\}}LL) and circumferential strain (e{\{}open{\}}CC) were estimated during different stages: at rest, during reduced and increased cardiac inotropy induced by esmolol and dobutamine infusion, respectively, and during acute ischemia. Agreement between image-based and microcrystal-based strain estimates was evaluated by their linear correlation, indicating that all strain components could be estimated with acceptable accuracy (r= 0.69 for e{\{}open{\}}RR, r= 0.64 for e{\{}open{\}}LL and r= 0.62 for e{\{}open{\}}CC). These findings are comparable to the performance of the current state-of-the-art commercial 3-D speckle tracking methods. Furthermore, shape of the strain curves, timing of peak values and location of dysfunctional regions were identified well. Whether 3-D elastic registration performs better than 3-D block matching-based methodologies still remains to be proven. {\textcopyright} 2013 World Federation for Ultrasound in Medicine {\&} Biology.},
author = {Heyde, Brecht and Bouchez, Stefaan and Thieren, Sabine and Vandenheuvel, Michael and Jasaityte, Ruta and Barbosa, Daniel and Claus, Piet and Maes, Frederik and Wouters, Patrick and D'hooge, Jan},
doi = {10.1016/j.ultrasmedbio.2013.02.463},
issn = {1879291X},
journal = {Ultrasound in Medicine and Biology},
keywords = {Echocardiography,Elastic registration,Invivo,Sonomicrometry,Strain,Validation},
number = {9},
pages = {1688--1697},
title = {{Elastic image registration to quantify 3-d regional myocardial deformation from volumetric ultrasound: Experimental validation in an animal model}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {39},
year = {2013}
}
@article{Hilty2019,
abstract = {Direct assessment of capillary perfusion has been prioritized in hemodynamic management of critically ill patients in addition to optimizing blood flow on the global scale. Sublingual handheld vital microscopy has enabled online acquisition of moving image sequences of the microcirculation, including the flow of individual red blood cells in the capillary network. However, due to inherent content complexity, manual image sequence analysis remained gold standard, introducing inter-observer variability and precluding real-time image analysis for clinical therapy guidance. Here we introduce an advanced computer vision algorithm for instantaneous analysis and quantification of morphometric and kinetic information related to capillary blood flow in the sublingual microcirculation. We evaluated this technique in a porcine model of septic shock and resuscitation and cardiac surgery patients. This development is of high clinical relevance because it enables implementation of point-of-care goal-directed resuscitation procedures based on correction of microcirculatory perfusion in critically ill and perioperative patients.},
author = {Hilty, Matthias Peter and Guerci, Philippe and Ince, Yasin and Toraman, Fevzi and Ince, Can},
doi = {10.1038/s42003-019-0473-8},
issn = {23993642},
journal = {Communications Biology},
number = {1},
pages = {15},
title = {{MicroTools enables automated quantification of capillary density and red blood cell velocity in handheld vital microscopy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {2},
year = {2019}
}
@article{Hilty2017,
abstract = {Assessment of the microcirculation is a promising target for the hemodynamic management of critically ill patients. However, just as the sole reliance on macrocirculatory parameters, single static parameters of the microcirculation may not represent a sufficient guide. Our hypothesis was that by serial topical application of acetylcholine (ACH) and nitroglycerin (NG), the sublingual microcirculation can be challenged to determine its endothelial cell-dependent and smooth muscle-dependent physiological reserve capacity. In 41 healthy subjects, sublingual capillary microscopy was performed before and after topical application of ACH and NG. Total vessel density (TVD) was assessed in parallel using manual computer-assisted image analysis as well as a fully automated analysis pathway utilizing a newly developed computer algorithm. Flow velocity was assessed using space-time diagrams of the venules as well as the algorithm-based calculation of an average perfused speed indicator (APSI). No change in all measured parameters was detected after sublingual topical application of ACH. Sublingual topical application of NG however led to an increase in TVD, space-time diagram-derived venular flow velocity and APSI. No difference was detected in heart rate, blood pressure, and cardiac output as measured by echocardiography, as well as in plasma nitric oxide metabolite content before and after the topical application of ACH and NG. In healthy subjects, the sublingual microcirculatory physiological reserve can be assessed non-invasively by topical application of nitroglycerin without affecting systemic circulation.},
author = {Hilty, Matthias Peter and Pichler, Jacqueline and Ergin, Bulent and Hefti, Urs and Merz, Tobias Michael and Ince, Can and Maggiorini, Marco},
doi = {10.1186/s40635-017-0139-0},
issn = {2197-425X},
journal = {Intensive Care Medicine Experimental},
number = {1},
pages = {13},
title = {{Assessment of endothelial cell function and physiological microcirculatory reserve by video microscopy using a topical acetylcholine and nitroglycerin challenge}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {5},
year = {2017}
}
@article{Ho2016,
abstract = {Objective Previously we proposed using an interpolated average computed tomography (IACT) method as a low-dose alternate of cine average computed tomography (CACT) for PET attenuation correction (AC). This study aims to evaluate its performance for thoracic lesions with different characteristics in simulations and clinical patients. Materials and methods We used the XCAT phantom to simulate noisy fluorine-18Fluorodeoxyglucose (18F-FDG) distribution with respiratory motion amplitudes of 2 and 3 cm. Average activity and attenuation maps represented static PET and CACT, respectively. IACT was generated by the end-inspiration and end-expiration phases of the attenuation maps (HCT-in and HCT-ex) using a deformable registration method. Spherical lesions with diameters of 10 and 20 mm with four target-to-background ratios (TBRs) were simulated at four different locations individually, including the lower left lung, lower right lung, middle right lung, and upper right lung. Five patients with a total of six thoracic lesions were recruited. They were scanned 1 h after 315-480 MBq 18F-FDG injection. Simulated and clinical PET sinograms were reconstructed with AC using (i) CACT, (ii) IACT, and (iii) helical computed tomography (HCTs). The TBRs and mean standardized uptake value were analyzed. Results Significant artifacts were observed in PET HCTs from visual assessment. For both simulation and clinical study, PET IACT was more similar to PET CACT in terms of TBRs and mean standardized uptake value. The differences between CACT/IACT and HCTs were more significant for lesions located at the lower lungs. Conclusion The IACT is a robust and low-dose AC method for improved thoracic lesion localization and quantitation for a wide range of lesion characteristics.},
author = {Ho, Cobie Y.T. and Wu, Tung Hsin and Mok, Greta S.P.},
doi = {10.1097/MNM.0000000000000435},
issn = {14735628},
journal = {Nuclear Medicine Communications},
keywords = {PET/CT,attenuation correction,respiratory artifacts,simulations,thoracic lesions},
number = {3},
pages = {297--306},
title = {{Interpolated average CT for PET attenuation correction in different lesion characteristics}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {37},
year = {2016}
}
@article{Ho2018,
abstract = {Decompressive craniectomy (DC) is a neurosurgical procedure performed to relieve the intracranial pressure engendered by brain swelling. However, no easy and accurate method exists for determining the craniectomy surface area. In this study, we implemented and compared three methods of estimating the craniectomy surface area for evaluating the decompressive effort. We collected 118 sets of preoperative and postoperative brain computed tomography images from patients who underwent craniectomy procedures between April 2009 and April 2011. The surface area associated with each craniectomy was estimated using the marching cube and quasi-Monte Carlo methods. The surface area was also estimated using a simple AC method, in which the area is calculated by multiplying the craniectomy length (A) by its height (C). The estimated surface area ranged from 9.46 to 205.32 cm2, with a median of 134.80 cm2. The root-mean-square deviation (RMSD) between the marching cube and quasi-Monte Carlo methods was 7.53 cm2. Furthermore, the RMSD was 14.45 cm2 between the marching cube and AC methods and 12.70 cm2 between the quasi-Monte Carlo and AC methods. Paired t-tests indicated no statistically significant difference between these methods. The marching cube and quasi-Monte Carlo methods yield similar results. The results calculated using the AC method are also clinically acceptable for estimating the DC surface area. Our results can facilitate additional studies on the association of decompressive effort with the effect of craniectomy.},
author = {Ho, Meng Yin and Tseng, Wei Lung and Xiao, Furen},
doi = {10.1155/2018/5237693},
issn = {16874196},
journal = {International Journal of Biomedical Imaging},
pages = {8},
title = {{Estimation of the Craniectomy Surface Area by Using Postoperative Images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {2018},
year = {2018}
}
@article{Hoinkiss2019,
abstract = {The sensitivity to subject motion is one of the major challenges in functional MRI (fMRI) studies in which a precise alignment of images from different time points is required to allow reliable quantification of brain activation throughout the scan. Especially the long measurement times and laborious fMRI tasks add to the amount of subject motion found in typical fMRI measurements, even when head restraints are used. In case of moving subjects, prospective motion correction can maintain the relationship between spatial image information and subject anatomy by constantly adapting the image slice positioning to follow the subject in real time. Image-based prospective motion correction is well-established in fMRI studies and typically computes the motion estimates based on a volume-to-volume image registration, resulting in low temporal resolution. This study combines fMRI using simultaneous multislice imaging with multislice-to-volume-based image registration to allow sub-TR motion detection with subsequent real-time adaption of the imaging system. Simultaneous multislice imaging is widely used in fMRI studies and, together with multislice-to-volume-based image registration algorithms, enables computing suitable motion states after only a single readout by registering the simultaneously excited slices to a reference volume acquired at the start of the measurement. The technique is evaluated in three human BOLD fMRI studies (n = 1, 5, and 1) to explore different aspects of the method. It is compared to conventional, volume-to-volume-based prospective motion correction as well as retrospective motion correction methods. Results show a strong reduction in retrospectively computed residual motion parameters of up to 50{\%} when comparing the two prospective motion correction techniques. An analysis of temporal signal-to-noise ratio as well as brain activation results shows high consistency between the results before and after additional retrospective motion correction when using the proposed technique, indicating successful prospective motion correction. The comparison of absolute tSNR values does not show an improvement compared to using retrospective motion correction alone. However, the improved temporal resolution may provide improved tSNR in the presence of more exaggerated intra-volume motion.},
author = {Hoinkiss, Daniel Christopher and Erhard, Peter and Breutigam, Nora Josefin and von Samson-Himmelstjerna, Federico and G{\"{u}}nther, Matthias and Porter, David Andrew},
doi = {10.1016/j.neuroimage.2019.06.042},
issn = {10959572},
journal = {NeuroImage},
keywords = {BOLD,EPI,Functional MRI,Image registration,Kalman filter,Multislice-to-volume,Prospective motion correction,Real-time MRI sequences,Simultaneous multislice (SMS)},
pages = {159--173},
title = {{Prospective motion correction in functional MRI using simultaneous multislice imaging and multislice-to-volume image registration}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {200},
year = {2019}
}
@article{Hoinkiss2017,
abstract = {Purpose: Prospective motion correction reduces artifacts in MRI by correcting for subject motion in real time, but techniques are limited for multishot 2-dimensional (2D) sequences. This study addresses this limitation by using 2D echo-planar imaging (EPI) slice navigator acquisitions together with a multislice-to-volume image registration. Methods: The 2D-EPI navigators were integrated into 2D imaging sequences to allow a rapid, real-time motion correction based on the registration of three navigator slices to a reference volume. A dedicated slice-iteration scheme was used to limit mutual spin-saturation effects between navigator and image data. The method was evaluated using T2-weighted spin echo and multishot rapid acquisition with relaxation enhancement (RARE) sequences, and its motion-correction capabilities were compared with those of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER). Validation was performed in vivo using a well-defined motion protocol. Results: Data acquired during subject motion showed residual motion parameters within ±0.5 mm and ±0.5°, and demonstrated a substantial improvement in image quality compared with uncorrected scans. In a comparison to PROPELLER, the proposed technique preserved a higher level of anatomical detail in the presence of subject motion. Conclusions: EPI-navigator-based prospective motion correction using multislice-to-volume image registration can substantially reduce image artifacts, while minimizing spin-saturation effects. The method can be adapted for use in other 2D MRI sequences and promises to improve image quality in routine clinical examinations. Magn Reson Med 78:2127–2135, 2017. {\textcopyright} 2017 International Society for Magnetic Resonance in Medicine.},
author = {Hoinkiss, Daniel Christopher and Porter, David Andrew},
doi = {10.1002/mrm.26951},
issn = {15222594},
journal = {Magnetic Resonance in Medicine},
keywords = {EPI navigator,RARE,motion artifacts,mutual information,prospective acquisition correction,real-time feedback},
number = {6},
pages = {2127--2135},
title = {{Prospective motion correction in 2D multishot MRI using EPI navigators and multislice-to-volume image registration}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {78},
year = {2017}
}
@article{Hsieh2011,
abstract = {Background: In recent years, magnetic resonance imaging (MRI) has become important in brain tumor diagnosis. Using this modality, physicians can locate specific pathologies by analyzing differences in tissue character presented in different types of MR images. This paper uses an algorithm integrating fuzzy-c-mean (FCM) and region growing techniques for automated tumor image segmentation from patients with menigioma. Only non-contrasted T1 and T2 -weighted MR images are included in the analysis. The study's aims are to correctly locate tumors in the images, and to detect those situated in the midline position of the brain. Methods. The study used non-contrasted T1- and T2-weighted MR images from 29 patients with menigioma. After FCM clustering, 32 groups of images from each patient group were put through the region-growing procedure for pixels aggregation. Later, using knowledge-based information, the system selected tumor-containing images from these groups and merged them into one tumor image. An alternative semi-supervised method was added at this stage for comparison with the automatic method. Finally, the tumor image was optimized by a morphology operator. Results from automatic segmentation were compared to the "ground truth" (GT) on a pixel level. Overall data were then evaluated using a quantified system. Results: The quantified parameters, including the "percent match" (PM) and "correlation ratio" (CR), suggested a high match between GT and the present study's system, as well as a fair level of correspondence. The results were compatible with those from other related studies. The system successfully detected all of the tumors situated at the midline of brain. Six cases failed in the automatic group. One also failed in the semi-supervised alternative. The remaining five cases presented noticeable edema inside the brain. In the 23 successful cases, the PM and CR values in the two groups were highly related. Conclusions: Results indicated that, even when using only two sets of non-contrasted MR images, the system is a reliable and efficient method of brain-tumor detection. With further development the system demonstrates high potential for practical clinical use. {\textcopyright} 2011 Hsieh et al; licensee BioMed Central Ltd.},
author = {Hsieh, Thomas M. and Liu, Yi Min and Liao, Chun Chih and Xiao, Furen and Chiang, I. Jen and Wong, Jau Min},
doi = {10.1186/1472-6947-11-54},
issn = {14726947},
journal = {BMC Medical Informatics and Decision Making},
number = {1},
pages = {12},
title = {{Automatic segmentation of meningioma from non-contrasted brain MRI integrating fuzzy clustering and region growing}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {11},
year = {2011}
}
@article{Huber2017,
abstract = {Adult-onset vanishing white-matter disease (VWM) is a rare autosomal recessive disease with neurological symptoms such as ataxia and paraparesis, showing extensive white-matter hyperintensities (WMH) on magnetic resonance (MR) imaging. Besides symptom-specific scores like the International Cooperative Ataxia Rating Scale (ICARS), there is no established tool to monitor disease progression. Because of extensive WMH, visual comparison of MR images is challenging. Here, we report the results of an automated method of segmentation to detect alterations in T2-weighted fluid-attenuated-inversion-recovery (FLAIR) sequences in a one-year follow-up study of a clinically stable patient with genetically diagnosed VWM. Signal alterations in MR imaging were quantified with a recently published WMH segmentation method by means of extreme value distribution (EVD). Our analysis revealed progressive FLAIR alterations of 5.84{\%} in the course of one year, whereas no significant WMH change could be detected in a stable multiple sclerosis (MS) control group. This result demonstrates that automated EVD-based segmentation allows a precise and rapid quantification of extensive FLAIR alterations like in VWM and might be a powerful tool for the clinical and scientific monitoring of degenerative white-matter diseases and potential therapeutic interventions.},
author = {Huber, Thomas and Herwerth, Marina and Alberts, Esther and Kirschke, Jan S. and Zimmer, Claus and Ilg, Ruediger},
doi = {10.1177/1971400916678222},
issn = {19714009},
journal = {Neuroradiology Journal},
keywords = {Extreme value distribution,automated segmentation,vanishing white-matter disease,white-matter hyperintensities},
number = {1},
pages = {5--9},
title = {{Automated segmentation reveals silent radiographic progression in adult-onset vanishing white-matter disease}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {30},
year = {2017}
}
@article{Huellebrand2020,
abstract = {Background and objective: Cardiovascular imaging is an exponentially growing field with aspects ranging from image acquisition and analysis to disease characterization, and evaluation of therapy approaches.The transfer of innovative new technological and algorithmic solutions into clinical practice is still slow. In addition to the verification of solutions, their integration in the clinical processing workflow must be enabled for the assessment of clinical impact and risks. The goal of our software platform for cardiac image processing – CAIPI – is to support researchers from different specialties such as imaging physics, computer science, and medicine by a common extensible platform to address typical challenges and hurdles in interdisciplinary cardiovascular imaging research. It provides an integrated solution for method comparison, integrated analysis, and validation in the clinical context. The interface concept enables a combination with existing frameworks that address specific aspects of the pipeline, such as modeling (e.g., OpenCMISS, CARP) or image reconstruction (Gadgetron). Methods: In our platform, we developed a concept for import, integration, and management of cardiac image data. The integration approach considers the spatiotemporal properties of the beating heart through a specific data model. The solution is based on MeVisLab and provides functionalities for data retrieval and storage. Two types of plugins can be added. While ToolPlugins usually provide processing algorithms such as image correction and segmentation, AnalysisPlugins enable interactive data exploration and reporting. GUI integration concepts are presented for both plugin types. We developed domain-specific reporting and visualization tools (e.g., AHA segment model) to enable validation studies by clinical experts. The platform offers plugins for calculating and reporting quantitative parameters such as cardiac function, which can be used to, e.g., evaluate the effect of processing algorithms on clinical parameters. Export functionalities include quantitative measurements to Excel, image data to PACS, and STL models to modeling and simulation tools. Results: To demonstrate the applicability of this concept both for method development and clinical application, we present use cases representing different problems along the innovation chain in cardiac MR imaging. Validation of an image reconstruction method (MRI T1 mapping) Validation of an image correction method for real-time 2D-PC MRI Comparison of quantification methods for blood flow analysis Training and integration of machine learning solutions with expert annotations Clinical studies with new imaging techniques (flow measurements in the carotid arteries and peripheral veins as well as cerebral spinal fluid). Conclusion: The presented platform can be used in interdisciplinary teams, in which engineers or data scientists perform the method validation, followed by clinical research studies in patient collectives. The demonstrated use cases show how it enables the transfer of innovations through validation in the cardiovascular application context.},
author = {Huellebrand, Markus and Messroghli, Daniel and Tautz, Lennart and Kuehne, Titus and Hennemuth, Anja},
doi = {10.1016/j.cmpb.2019.105277},
issn = {18727565},
journal = {Computer Methods and Programs in Biomedicine},
keywords = {Cardiology,Image processing,MRI,Medical image analysis,Segmentation},
pages = {13},
title = {{An extensible software platform for interdisciplinary cardiovascular imaging research}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {184},
year = {2020}
}
@article{Humbert2016,
abstract = {Purpose: Cortical thickness and density are critical components in determining the strength of bony structures. Computed tomography (CT) is one possible modality for analyzing the cortex in 3D. In this paper, a model-based approach for measuring the cortical bone thickness and density from clinical CT images is proposed. Methods: Density variations across the cortex were modeled as a function of the cortical thickness and density, location of the cortex, density of surrounding tissues, and imaging blur. High resolution micro-CT data of cadaver proximal femurs were analyzed to determine a relationship between cortical thickness and density. This thickness-density relationship was used as prior information to be incorporated in the model to obtain accurate measurements of cortical thickness and density from clinical CT volumes. The method was validated using micro-CT scans of 23 cadaver proximal femurs. Simulated clinical CT images with different voxel sizes were generated from the micro-CT data. Cortical thickness and density were estimated from the simulated images using the proposed method and compared with measurements obtained using the micro-CT images to evaluate the effect of voxel size on the accuracy of the method. Then, 19 of the 23 specimens were imaged using a clinical CT scanner. Cortical thickness and density were estimated from the clinical CT images using the proposed method and compared with the micro-CT measurements. Finally, a case-control study including 20 patients with osteoporosis and 20 age-matched controls with normal bone density was performed to evaluate the proposed method in a clinical context. Results: Cortical thickness (density) estimation errors were 0.07 ± 0.19 mm (-18 ± 92 mg/cm3) using the simulated clinical CT volumes with the smallest voxel size (0.33 × 0.33 × 0.5 mm3), and 0.10 ± 0.24 mm (-10 ± 115 mg/cm3) using the volumes with the largest voxel size (1.0 × 1.0 × 3.0 mm3). A trend for the cortical thickness and density estimation errors to increase with voxel size was observed and was more pronounced for thin cortices. Using clinical CT data for 19 of the 23 samples, mean errors of 0.18 ± 0.24 mm for the cortical thickness and 15 ± 106 mg/cm3 for the density were found. The case-control study showed that osteoporotic patients had a thinner cortex and a lower cortical density, with average differences of -0.8 mm and -58.6 mg/cm3 at the proximal femur in comparison with age-matched controls (p-value {\textless} 0.001). Conclusions: This method might be a promising approach for the quantification of cortical bone thickness and density using clinical routine imaging techniques. Future work will concentrate on investigating how this approach can improve the estimation of mechanical strength of bony structures, the prevention of fracture, and the management of osteoporosis.},
author = {Humbert, Ludovic and {Hazrati Marangalou}, Javad and {Del R{\'{i}}o Barquero}, Luis Miguel and {Van Lenthe}, G. Harry and {Van Rietbergen}, Bert},
doi = {10.1118/1.4944501},
issn = {00942405},
journal = {Medical Physics},
keywords = {bone mineral density,computed tomography,cortical thickness,hip fracture,osteoporosis},
number = {4},
pages = {1945--1954},
title = {{Technical Note: Cortical thickness and density estimation from clinical CT using a prior thickness-density relationship}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2016}
}
@article{Humbert2017,
abstract = {The 3D distribution of the cortical and trabecular bone mass in the proximal femur is a critical component in determining fracture resistance that is not taken into account in clinical routine Dual-energy X-ray Absorptiometry (DXA) examination. In this paper, a statistical shape and appearance model together with a 3D-2D registration approach are used to model the femoral shape and bone density distribution in 3D from an anteroposterior DXA projection. A model-based algorithm is subsequently used to segment the cortex and build a 3D map of the cortical thickness and density. Measurements characterising the geometry and density distribution were computed for various regions of interest in both cortical and trabecular compartments. Models and measurements provided by the '3D-DXA' software algorithm were evaluated using a database of 157 study subjects, by comparing 3D-DXA analyses (using DXA scanners from three manufacturers) with measurements performed by Quantitative Computed Tomography (QCT). The mean point-to-surface distance between 3D-DXA and QCT femoral shapes was 0.93 mm. The mean absolute error between cortical thickness and density estimates measured by 3D-DXA and QCT was 0.33 mm and 72 mg/cm3. Correlation coefficients (R) between the 3D-DXA and QCT measurements were 0.86, 0.93, and 0.95 for the volumetric bone mineral density at the trabecular, cortical, and integral compartments respectively, and 0.91 for the mean cortical thickness. 3D-DXA provides a detailed analysis of the proximal femur, including a separate assessment of the cortical layer and trabecular macrostructure, which could potentially improve osteoporosis management while maintaining DXA as the standard routine modality.},
author = {Humbert, Ludovic and Martelli, Yves and Fonolla, Roger and Steghofer, Martin and {DI Gregorio}, Silvana and Malouf, Jorge and Romera, Jordi and Barquero, Luis Miguel Del Rio},
doi = {10.1109/TMI.2016.2593346},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Bone mineral density,DXA,cortical thickness,image registration,osteoporosis,proximal femur},
number = {1},
pages = {27--39},
title = {{3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {36},
year = {2017}
}
@article{Ige2017,
abstract = {This work investigated the effects of fibre type, dosage and maximum aggregate size on the mechanical behaviour of concrete reinforced with steel fibres. Hooked-end steel fibres with 50 and 60 mm length and aspect ratios (length/diameter) of 45, 65 and 80 were used with maximum sizes of coarse aggregate of 10 and 20 mm. The same mix proportions of concrete were used throughout the investigation. Flexural testing of 600 mm square panels was performed. Subsequently, cores were taken from these panels and X-ray computed tomography was used to analyse the positioning of fibres in hardened concrete. The experimental results show that the performance of steel fibre-reinforced concrete improved drastically when compared to plain concrete without fibres. Longer, thinner fibres and smaller aggregates were noted to give the best results.},
author = {Ige, Olubisi and Barnett, Stephanie and Chiverton, John and Nassif, Ayman and Williams, John},
doi = {10.1080/17436753.2017.1284389},
issn = {17436761},
journal = {Advances in Applied Ceramics},
keywords = {Concrete,X-ray CT,flexural properties,steel fibres},
number = {4},
pages = {193--198},
title = {{Effects of steel fibre-aggregate interaction on mechanical behaviour of steel fibre reinforced concrete}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {116},
year = {2017}
}
@article{Iversen2018,
abstract = {Purpose: In neurosurgery, reliable information about blood vessel anatomy and flow direction is important to identify, characterize, and avoid damage to the vasculature. Due to ultrasound Doppler angle dependencies and the complexity of the vascular architecture, clinically valuable 3-D flow direction information is currently not available. In this paper, we aim to clinically validate and demonstrate the intraoperative use of a fully automatic method for estimation of 3-D blood flow direction from freehand 2-D Doppler ultrasound. Methods: A 3-D vessel model is reconstructed from 2-D Doppler ultrasound and used to determine the vessel architecture. The blood flow direction is then estimated automatically using the model in combination with Doppler velocity data. To enable testing and validation during surgery, the method was implemented as part of the open-source navigation system CustusX (www.custusx.org). Results: Ten patients were included prospectively. Data from four patients were processed postoperatively, and data from six patients were processed intraoperatively. In total, the blood flow direction was estimated for 48 different blood vessels with a success rate of 98{\%}. Conclusions: In this work, we have shown that the proposed method is suitable for fully automatic estimation of the blood flow direction in intracranial vessels during neurosurgical interventions. The method has the potential to make the understanding of the complex vascular anatomy and flow pattern more intuitive for the surgeon. The method is compatible with intraoperative use, and results can be presented within the limited time frame where they still are of clinical interest.},
author = {Iversen, Daniel H{\o}yer and L{\o}vstakken, Lasse and Unsg{\aa}rd, Geirmund and Reinertsen, Ingerid},
doi = {10.1007/s11548-018-1711-0},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Blood flow,Intraoperative,Neurosurgery,Ultrasound},
number = {5},
pages = {693--701},
title = {{Automatic intraoperative estimation of blood flow direction during neurosurgical interventions}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2018}
}
@inproceedings{Izard,
abstract = {The visualization of the radiological results with more advanced techniques than the current ones, such as Augmented Reality and Virtual Reality technologies, represent a great advance for medical professionals, by eliminating their imagination capacity as an indispensable requirement for the understanding of medical images. The problem is that for its application it is necessary to segment the anatomical areas of interest, and this currently involves the intervention of the human being. The Nextmed project is presented as a complete solution that includes DICOM images import, automatic segmentation of certain anatomical structures, 3D mesh generation of the segmented area, visualization engine with Augmented Reality and Virtual Reality, all thanks to different software platforms that have been implemented and detailed, including results obtained from real patients. We will focus on the visualization platform using both Augmented and Virtual Reality technologies to allow medical professionals to work with 3d model representation of medical images in a different way taking advantage of new technologies.},
author = {Izard, Santiago Gonz{\'{a}}lez and Plaza, Scar Alonso and Torres, Ramiro S{\'{a}}nchez and M{\'{e}}ndez, Juan Antonio Juanes and Garc{\'{i}}a-Pẽalvo, Francisco Jos{\'{e}}},
booktitle = {ACM International Conference Proceeding Series},
doi = {10.1145/3362789.3362936},
isbn = {9781450371919},
keywords = {Augmented Reality,Automatic Segmentation,Medical Imaging,Virtual Reality},
pages = {459--468},
title = {{NextMed, Augmented and Virtual Reality platform for 3D medical imaging visualization: Explanation of the software platform developed for 3D models visualization related with medical images using Augmented and Virtual Reality technology}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075443653{\&}doi=10.1145{\%}2F3362789.3362936{\&}partnerID=40{\&}md5=4c4690e277fd6b868cb7be97c738a3ed},
year = {2019}
}
@incollection{Jackowski2005,
abstract = {Automatic image segmentation methods often involve errors, requiring the assistance of the user to correct them. In this paper, a computer-aided design system is introduced for correcting such errors. The proposed system approximates each 3-D region by a parametric surface. Region voxels are first parametrized spherically using a coarseto-fine subdivision method. By using the voxel positions and their parameter coordinates, control points of a rational Gaussian surface are determined through a least-squares method to approximate the region. Finally, this surface is overlaid with the volumetric image and by locally pulling or pushing it with the mouse while viewing image information, the surface is revised as needed. Typically, a few minutes are sufficient to correct errors in a region. {\textcopyright} Springer-Verlag Berlin Heidelberg 2005.},
author = {Jackowski, Marcel and Goshtasby, Ardeshir},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/11566489_88},
isbn = {3540293264},
issn = {03029743},
pages = {717--724},
title = {{A computer-aided design system for revision of segmentation errors}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33744812176{\&}doi=10.1007{\%}2F11566489{\_}88{\&}partnerID=40{\&}md5=1a20beb704d367c3bb74ae1934b0a84d},
volume = {3750 LNCS},
year = {2005}
}
@article{Jakubicek2018,
abstract = {{\textcopyright} 2018 Bentham Science Publishers. Background: Robust and accurate segmentation of the spine subdivided into individual vertebrae is necessary for subsequent diagnosis of illnesses related to the spine, particularly those requiring detection and classification of bone lesions. Based on correct vertebra segmentation, the current status of a disease under treatment-as well as its progress-can be determined and followed. Discussion: The problem is complicated by frequent heavy deformations of both the spine axis and individual vertebrae due to illness, so that some vertebrae may differ substantially from expected shapes or even be missing. This overview summarises and discusses so far published methods for spine and vertebrae segmentation in 3D CT thoracic data. Conclusion: It suggests a classification of these algorithms based on the used approaches, complexity of algorithms, as well as on achieved efficiencies.},
author = {Jakubicek, Roman and Chmelik, Jiri and Jan, Jiri},
doi = {10.2174/1573405613666170622120228},
issn = {15734056},
journal = {Current Medical Imaging Reviews},
number = {6},
pages = {853--866},
title = {{Vertebrae Segmentation in 3D CT Data: A Review of Methods and Evaluation Approaches}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {14},
year = {2017}
}
@inproceedings{Johnson,
abstract = {Computers have changed the way we live, work, and even recreate. Now, they are transforming how we think about and treat human disease. In particular, advanced techniques in biomedical computing, imaging, and visualization are changing the face of biology and medicine in both research and clinical practice. The goals of biomedical computing, imaging and visualization are multifaceted. While some images and visualizations facilitate diagnosis, others help physicians plan surgery. Biomedical simulations can help to acquire a better understanding of human physiology. Still other biomedical computing and visualization techniques are used for medical training. Within biomedical research, new computational technologies allow us to "see" into and understand our bodies with unprecedented depth and detail. As a result of these advances, biomedical computing and visualization will help produce exciting new biomedical scientific discoveries and clinical treatments. In this paper, we give an overview of the computational science pipeline for an application in neuroscience and present associated research results in medical imaging, modeling, simulation, and visualization. Copyright {\textcopyright} 2006, Australian Computer Society, Inc.},
author = {Johnson, Chris R. and Weinstein, David M.},
booktitle = {Conferences in Research and Practice in Information Technology Series},
doi = {10.1109/pact.2003.1238017},
isbn = {1920682309},
issn = {14451336},
keywords = {Biomedical computing,Imaging,Problem solving environment,Visualization},
pages = {3--10},
title = {{Biomedical computing and visualization}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868696757{\&}partnerID=40{\&}md5=4571d3f07f7e58d431d9e91206610690},
volume = {48},
year = {2006}
}
@article{Jones2015,
abstract = {Background: In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming. New method: We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links. Results: We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results. Comparison with existing methods: Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method. Conclusion: Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70{\%} time improvement when compared with other examples in publication.},
author = {Jones, Cory and Liu, Ting and Cohan, Nathaniel Wood and Ellisman, Mark and Tasdizen, Tolga},
doi = {10.1016/j.jneumeth.2015.03.005},
issn = {1872678X},
journal = {Journal of Neuroscience Methods},
keywords = {3D segmentation,Connectomics,Electron microscopy,Image segmentation,Neuron reconstruction,Semi-automatic segmentation},
pages = {13--21},
title = {{Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {246},
year = {2015}
}
@article{Joshi2018,
abstract = {Histogram-based methods can be used to analyse and transform medical images. Histogram specification is one such method which has been widely used to transform the histograms of cone beam CT (CBCT) images to match those of corresponding CT images. However, when the derived transformation is applied to the CBCT image pixels, significant artefacts can be produced. We propose the iterative peak combination algorithm, a novel and robust method for automatically identifying relevant features in medical image histograms. The procedure is conceptually simple and can be applied equally well to both CT and CBCT image histograms. We also demonstrate how iterative peak combination can be used to transform CBCT images in such as way as to improve the Hounsfield Unit (HU) calibration of CBCT image pixel values, without introducing additional artefacts. We analyse 36 pelvis CBCT images and show that the average difference in fat tissue pixel values between CT images and CBCT images processed using the iterative peak combination algorithm is 23.7 HU. Compared to 136.7 HU in unprocessed CBCT images and 50.9 in CBCT images processed using histogram specification.},
author = {Joshi, K. D. and Marchant, T. E.},
doi = {10.1088/2057-1976/aa929d},
issn = {20571976},
journal = {Biomedical Physics and Engineering Express},
keywords = {cone beam CT,histogram matching,image histogram,image processing},
number = {1},
pages = {10},
title = {{Iterative peak combination: A robust technique for identifying relevant features in medical image histograms}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {4},
year = {2018}
}
@article{Ju2006,
abstract = {Sectioning tissues for optical microscopy often introduces upon the resulting sections distortions that make 3D reconstruction difficult. Here we present an automatic method for producing a smooth 3D volume from distorted 2D sections in the absence of any undistorted references. The method is based on pairwise elastic image warps between successive tissue sections, which can be computed by 2D image registration. Using a Gaussian filter, an average warp is computed for each section from the pairwise warps in a group of its neighboring sections. The average warps deform each section to match its neighboring sections, thus creating a smooth volume where corresponding features on successive sections lie close to each other. The proposed method can be used with any existing 2D image registration method for 3D reconstruction. In particular, we present a novel image warping algorithm based on dynamic programming that extends Dynamic Time Warping in 1D speech recognition to compute pairwise warps between high-resolution 2D images. The warping algorithm efficiently computes a restricted class of 2D local deformations that are characteristic between successive tissue sections. Finally, a validation framework is proposed and applied to evaluate the quality of reconstruction using both real sections and a synthetic volume. {\textcopyright} 2006 Elsevier B.V. All rights reserved.},
author = {Ju, Tao and Warren, Joe and Carson, James and Bello, Musodiq and Kakadiaris, Ioannis and Chiu, Wah and Thaller, Christina and Eichele, Gregor},
doi = {10.1016/j.jneumeth.2006.02.020},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
keywords = {3D reconstruction,Dynamic programming,Filtering,Histology,Image warping},
number = {1-2},
pages = {84--100},
title = {{3D volume reconstruction of a mouse brain from histological sections using warp filtering}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-33747181686{\&}doi=10.1016{\%}2Fj.jneumeth.2006.02.020{\&}partnerID=40{\&}md5=a4e90f7b3d3b71adcc36d27a722e9843},
volume = {156},
year = {2006}
}
@article{Jug2014,
abstract = {Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development. {\textcopyright} 2014 The Authors.},
author = {Jug, Florian and Pietzsch, Tobias and Preibisch, Stephan and Tomancak, Pavel},
doi = {10.1016/j.ymeth.2014.04.004},
issn = {10959130},
journal = {Methods},
keywords = {Drosophila,Image analysis,Processing,Registration,Segmentation,Tracking},
number = {1},
pages = {60--73},
title = {{Bioimage Informatics in the context of Drosophila research}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {68},
year = {2014}
}
@article{Jun2017,
abstract = {We propose the use of ensemble classifiers to overcome inter-scanner variations in the differentiation of regional disease patterns in high-resolution computed tomography (HRCT) images of diffuse interstitial lung disease patients obtained from different scanners. A total of 600 rectangular 20 × 20-pixel regions of interest (ROIs) on HRCT images obtained from two different scanners (GE and Siemens) and the whole lung area of 92 HRCT images were classified as one of six regional pulmonary disease patterns by two expert radiologists. Textual and shape features were extracted from each ROI and the whole lung parenchyma. For automatic classification, individual and ensemble classifiers were trained and tested with the ROI dataset. We designed the following three experimental sets: an intra-scanner study in which the training and test sets were from the same scanner, an integrated scanner study in which the data from the two scanners were merged, and an inter-scanner study in which the training and test sets were acquired from different scanners. In the ROI-based classification, the ensemble classifiers showed better (p {\textless} 0.001) accuracy (89.73{\%}, SD = 0.43) than the individual classifiers (88.38{\%}, SD = 0.31) in the integrated scanner test. The ensemble classifiers also showed partial improvements in the intra- and inter-scanner tests. In the whole lung classification experiment, the quantification accuracies of the ensemble classifiers with integrated training (49.57{\%}) were higher (p {\textless} 0.001) than the individual classifiers (48.19{\%}). Furthermore, the ensemble classifiers also showed better performance in both the intra- and inter-scanner experiments. We concluded that the ensemble classifiers provide better performance when using integrated scanner images.},
author = {Jun, Sanghoon and Kim, Namkug and Seo, Joon Beom and Lee, Young Kyung and Lynch, David A.},
doi = {10.1007/s10278-017-9957-6},
issn = {1618727X},
journal = {Journal of Digital Imaging},
keywords = {Ensemble learning,Inter-scanner variation,Interstitial lung disease (ILD),Multi-center trial,Support vector machine (SVM)},
number = {6},
pages = {761--771},
title = {{An Ensemble Method for Classifying Regional Disease Patterns of Diffuse Interstitial Lung Disease Using HRCT Images from Different Vendors}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {30},
year = {2017}
}
@article{Jung2019,
abstract = {BACKGROUND AND PURPOSE: The anterior cingulate cortex (ACC) is involved in several cognitive processes including executive function. Degenerative changes of ACC are consistently seen in Alzheimer's disease (AD). However, volumetric changes specific to the ACC in AD are not clear because of the difficulty in segmenting this region. The objectives of the current study were to develop a precise and high-throughput approach for measuring ACC volumes and to correlate the relationship between ACC volume and cognitive function in AD. METHODS: Structural T 1 -weighted magnetic resonance images of AD patients (n = 47) and age-matched controls (n = 47) at baseline and at 24 months were obtained from the Alzheimer's disease neuroimaging initiative (ADNI) database and studied using a custom-designed semiautomated segmentation protocol. RESULTS: ACC volumes obtained using the semiautomated protocol were highly correlated to values obtained from manual segmentation (r =.98) and the semiautomated protocol was considerably faster. When comparing AD and control subjects, no significant differences were observed in baseline ACC volumes or in change in ACC volumes over 24 months using the two segmentation methods. However, a change in ACC volume over 24 months did not correlate with a change in mini-mental state examination scores. CONCLUSIONS: Our results indicate that the proposed semiautomated segmentation protocol is reliable for determining ACC volume in neurodegenerative conditions including AD.},
author = {Jung, Flora and Kazemifar, Samaneh and Bartha, Robert and Rajakumar, Nagalingam},
doi = {10.1111/jon.12598},
issn = {15526569},
journal = {Journal of Neuroimaging},
keywords = {MRI,automated segmentation,brain atrophy,structural biomarker},
number = {3},
pages = {376--382},
title = {{Semiautomated Assessment of the Anterior Cingulate Cortex in Alzheimer's Disease}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {29},
year = {2019}
}
@article{Kainz2015,
abstract = {Image and signal analysis applications are substantial in scientific research. Both open source and commercial packages provide a wide range of functions for image and signal analysis, which are sometimes supported very well by the communities in the corresponding fields. Commercial software packages have the major drawback of being expensive and having undisclosed source code, which hampers extending the functionality if there is no plugin interface or similar option available. However, both variants cannot cover all possible use cases and sometimes custom developments are unavoidable, requiring open source applications. In this paper we describe IQM, a completely free, portable and open source (GNU GPLv3) image and signal analysis application written in pure Java. IQM does not depend on any natively installed libraries and is therefore runnable out-of-the-box. Currently, a continuously growing repertoire of 50 image and 16 signal analysis algorithms is provided. The modular functional architecture based on the three-tier model is described along the most important functionality. Extensibility is achieved using operator plugins, and the development of more complex workflows is provided by a Groovy script interface to the JVM. We demonstrate IQM's image and signal processing capabilities in a proof-of-principle analysis and provide example implementations to illustrate the plugin framework and the scripting interface. IQM integrates with the popular ImageJ image processing software and is aiming at complementing functionality rather than competing with existing open source software. Machine learning can be integrated into more complex algorithms via the WEKA software package as well, enabling the development of transparent and robust methods for image and signal analysis.},
author = {Kainz, Philipp and Mayrhofer-Reinhartshuber, Michael and Ahammer, Helmut},
doi = {10.1371/journal.pone.0116329},
issn = {19326203},
journal = {PLoS ONE},
number = {1},
pages = {28},
title = {{IQM: An extensible and portable open source application for image and signal analysis in java}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {10},
year = {2015}
}
@article{Kankaanpaa2012,
abstract = {BioImageXD puts open-source computer science tools for three-dimensional visualization and analysis into the hands of all researchers, through a user-friendly graphical interface tuned to the needs of biologists. BioImageXD has no restrictive licenses or undisclosed algorithms and enables publication of precise, reproducible and modifiable workflows. It allows simple construction of processing pipelines and should enable biologists to perform challenging analyses of complex processes. We demonstrate its performance in a study of integrin clustering in response to selected inhibitors. {\textcopyright} 2012 Nature America, Inc. All rights reserved.},
author = {Kankaanp{\"{a}}{\"{a}}, Pasi and Paavolainen, Lassi and Tiitta, Silja and Karjalainen, Mikko and P{\"{a}}iv{\"{a}}rinne, Joacim and Nieminen, Jonna and Marjom{\"{a}}ki, Varpu and Heino, Jyrki and White, Daniel J.},
doi = {10.1038/nmeth.2047},
issn = {15487091},
journal = {Nature Methods},
number = {7},
pages = {683--689},
title = {{BioImageXD: An open, general-purpose and high-throughput image-processing platform}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {9},
year = {2012}
}
@article{Kazemifar2014,
abstract = {Background: The change in volume of anatomic structures is as a sensitive indicator of Alzheimer disease (AD) progression. Although several methods are available to measure brain volumes, improvements in speed and automation are required. Our objective was to develop a fully automated, fast, and reliable approach to measure change in medial temporal lobe (MTL) volume, including primarily hippocampus. Methods: The MTL volume defined in an atlas image was propagated onto each baseline image and a level set algorithm was applied to refine the shape and smooth the boundary. The MTL of the baseline image was then mapped onto the corresponding follow-up image to measure volume change ($\delta$MTL). Baseline and 24 months 3D T1-weighted images from the Alzheimer Disease Neuroimaging Initiative (ADNI) were randomly selected for 50 normal elderly controls (NECs), 50 subjects with mild cognitive impairment (MCI) and 50 subjects with AD to test the algorithm. The method was compared to the FreeSurfer segmentation tools. Results: The average $\delta$MTL (mean±SEM) was 68±35mm3 in NEC, 187±38mm3 in MCI and 300±34mm3 in the AD group and was significantly different (p{\textless}0.0001) between all three groups. The $\delta$MTL was correlated with cognitive decline. Comparison with existing method(s): Results for the FreeSurfer software were similar but did not detect significant differences between the MCI and AD groups. Conclusion: This novel segmentation approach is fully automated and provides a robust marker of brain atrophy that shows different rates of atrophy over 2 years between NEC, MCI, and AD groups. {\textcopyright} 2014 Elsevier B.V.},
author = {Kazemifar, Samaneh and Drozd, John J. and Rajakumar, Nagalingam and Borrie, Michael J. and Bartha, Robert},
doi = {10.1016/j.jneumeth.2014.01.033},
issn = {1872678X},
journal = {Journal of Neuroscience Methods},
keywords = {Alzheimer disease,Hippocampus,MRI,Medial temporal lobe segmentation,Multi-atlas,Shape},
pages = {35--46},
title = {{Automated algorithm to measure changes in medial temporal lobe volume in Alzheimer disease}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {227},
year = {2014}
}
@article{Kennedy2019,
abstract = {Purpose: To demonstrate selection of a small representative subset of images from a pool of images comprising a potential atlas (PA) pelvic CT set to be used for autosegmentation of a separate target image set. The aim is to balance the need for the atlas set to represent anatomical diversity with the need to minimize resources required to create a high quality atlas set (such as multiobserver delineation), while retaining access to additional information available for the PA image set. Methods: Preprocessing was performed for image standardization, followed by image registration. Clustering was used to select the subset that provided the best coverage of a target dataset as measured by postregistration image intensity similarities. Tests for clustering robustness were performed including repeated clustering runs using different starting seeds and clustering repeatedly using 90{\%} of the target dataset chosen randomly. Comparisons of coverage of a target set (comprising 711 pelvic CT images) were made for atlas sets of five images (chosen from a PA set of 39 pelvic CT and MR images) (a) at random (averaged over 50 random atlas selections), (b) based solely on image similarities within the PA set (representing prospective atlas development), (c) based on similarities within the PA set and between the PA and target dataset (representing retrospective atlas development). Comparisons were also made to coverage provided by the entire PA set of 39 images. Results: Exemplar selection was highly robust with exemplar selection results being unaffected by choice of starting seed with very occasional change to one of the exemplar choices when the target set was reduced. Coverage of the target set, as measured by best normalized cross-correlation similarity of target images to any exemplar image, provided by five well-selected atlas images (mean = 0.6497) was more similar to coverage provided by the entire PA set (mean = 0.6658) than randomly chosen atlas subsets (mean = 0.5977). This was true both of the mean values and the shape of the distributions. Retrospective selection of atlases (mean = 0.6497) provided a very small improvement over prospective atlas selection (mean = 0.6431). All differences were significant (P {\textless} 1.0E-10). Conclusions: Selection of a small representative image set from one dataset can be utilized to develop an atlas set for either retrospective or prospective autosegmentation of a different target dataset. The coverage provided by such a judiciously selected subset has the potential to facilitate propagation of numerous retrospectively defined structures, utilizing additional information available with multimodal imaging in the atlas set, without the need to create large atlas image sets.},
author = {Kennedy, Angel and Dowling, Jason and Greer, Peter B. and Holloway, Lois and Jameson, Michael G. and Roach, Dale and Ghose, Soumya and Rivest-H{\'{e}}nault, David and Marcello, Marco and Ebert, Martin A.},
doi = {10.1002/mp.13494},
issn = {00942405},
journal = {Medical Physics},
keywords = {autosegmentation,clustering,image registration,image-atlas},
number = {5},
pages = {2243--2250},
title = {{Similarity clustering-based atlas selection for pelvic CT image segmentation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {46},
year = {2019}
}
@inproceedings{Khajwaniya,
abstract = {The paper proposes a new methodology in order to improve the quality of satellite images. It is demonstrated that it is possible to achieve a better performance than that of Bilateral filter in a variety of noise levels. We have proposed Weiner filter in accordance with SPEA2 algorithm which removes pre-filtering and high noise level: therefore it improves the Peak Signal-to-Noise Ratio (PSNR) and visual quality gets improved and complexities and processing time are reduced. This improved algorithm is extended and used to denoise satellite images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite.},
author = {Khajwaniya, Kuldeep Kumar and Tiwari, Vibha},
booktitle = {Proceedings of 2015 IEEE 9th International Conference on Intelligent Systems and Control, ISCO 2015},
doi = {10.1109/ISCO.2015.7282324},
isbn = {9781479964802},
keywords = {Bilateral Filter,Image denoising,SPEA2 Algorithm,Weiner Filter},
title = {{Satellite image denoising using Weiner filter with SPEA2 algorithm}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959108119{\&}doi=10.1109{\%}2FISCO.2015.7282324{\&}partnerID=40{\&}md5=3cac197663c657dd57bf87eb47941101},
year = {2015}
}
@article{Kim2018,
abstract = {Purpose: This study aimed to develop a linac-mounted kilovoltage (kV) projection streaming-based tracking method for vertebral targets during spine stereotactic radiation surgery and evaluate the clinical feasibility of the proposed spine tracking method. Methods and materials: Using real-time kV projection streaming within XVI (Elekta XVI), kV–projection-based tracking was applied to the target vertebral bodies. Two-dimensional in-plane patient translation was calculated via an image registration between digitally reconstructed radiographs (DRRs) and kV projections. DRR was generated from the cone beam computed tomography (CBCT) scan, which was obtained immediately before the tracking session. During a tracking session, each kV projection was streamed for an intensity gradient-based image with similar metric-based registration to the offset DRR. The ground truth displacement for each kV beam angle was calculated at the beam isocenter using the 6 degrees-of-freedom transformation that was obtained by a CBCT-CBCT rigid registration. The resulting translation by the DRR-projection registration was compared with the ground truth displacement. The proposed tracking method was evaluated retrospectively and online, using 7 and 5 spine patients, respectively. Results: The accuracy and precision of spine tracking for in-plane patient motion were 0.5 ± 0.2 and 0.2 ± 0.1 mm. The magnitude of patient motion that was estimated using the CBCT-CBCT rigid registration was (0.5 ± 0.4, 0.4 ± 0.3, 0.3 ± 0.3) mm and (0.3 ± 0.4, 0.2 ± 0.2, 0.5 ± 0.6) mm for all tracking sessions. The intrafraction motion was within 2 mm for all CBCT scans considered. Conclusions: This study demonstrated that the proposed spine tracking method can track intrafraction motion with sub-millimeter accuracy and precision, and sub-second latency.},
author = {Kim, Jihun and Park, Yang Kyun and Edmunds, David and Oh, Kevin and Sharp, Gregory C. and Winey, Brian},
doi = {10.1016/j.adro.2018.06.002},
issn = {24521094},
journal = {Advances in Radiation Oncology},
number = {4},
pages = {682--692},
title = {{Kilovoltage projection streaming-based tracking application (KiPSTA): First clinical implementation during spine stereotactic radiation surgery}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050957688{\&}doi=10.1016{\%}2Fj.adro.2018.06.002{\&}partnerID=40{\&}md5=80de6701682b693312a12d441a486b1c},
volume = {3},
year = {2018}
}
@article{Kiss2017,
abstract = {Background: Developmental biology has made great strides in recent years towards the quantification of cellular properties during development. This requires tissues to be imaged and segmented to generate computerised versions that can be easily analysed. In this context, one of the principal technical challenges remains the faithful detection of cellular contours, principally due to variations in image intensity throughout the tissue. Watershed segmentation methods are especially vulnerable to these variations, generating multiple errors due notably to the incorrect detection of the outer surface of the tissue. Results: We use the level set method (LSM) to improve the accuracy of the watershed segmentation in different ways. First, we detect the outer surface of the tissue, reducing the impact of low and variable contrast at the surface during imaging. Second, we demonstrate a new edge function for a level set, based on second order derivatives of the image, to segment individual cells. Finally, we also show that the LSM can be used to segment nuclei within the tissue. Conclusion: The watershed segmentation of the outer cell layer is demonstrably improved when coupled with the LSM-based surface detection step. The tool can also be used to improve watershed segmentation at cell-scale, as well as to segment nuclei within a tissue. The improved segmentation increases the quality of analysis, and the surface detected by our algorithm may be used to calculate local curvature or adapted for other uses, such as mathematical simulations.},
author = {Kiss, Annam{\'{a}}ria and Moreau, Typhaine and Mirabet, Vincent and Calugaru, Cerasela Iliana and Boudaoud, Arezki and Das, Pradeep},
doi = {10.1186/s13007-017-0264-5},
issn = {17464811},
journal = {Plant Methods},
keywords = {3D,Cell,Cellwall,Confocal image,L1,Level set method,Nucleus,Segmentation,Watershed},
number = {1},
pages = {11},
title = {{Segmentation of 3D images of plant tissues at multiple scales using the level set method}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2017}
}
@article{Knopf2013,
abstract = {Advanced 4D dose calculations (4DDCs) for scanned particle therapy show that in the incidence of motion, it is insufficient to use target contours defined on one reference CT phase. ICRU Report 62 (ICRU 1999 ICRU Report 62 (Bethesda, MD: ICRU)) advises that variations in size, shape and position of CTVs relative to anatomic reference points have to be considered for internal target volumes (ITVs). In addition to geometrical margin adaption, changes of water equivalent path length have to be considered for particle therapy. Different ITV concepts have been applied to six representative patients (liver and lung indications) based on 4DCT. Geometrical ITVs (gITV) were calculated by combining deformed CTVs over all motion phases. To take into account path length changes, range adapted ITVs (raITV) were established as the union of range adapted CTVs in all phases. For gated delivery, gat-gITVs and gat-raITVs were calculated. Extensive 4DDCs have been performed for two exemplary patients to illustrate that neither re-scanning nor gating can sufficiently compensate for motion effects if no appropriate margins are employed and to evaluate the effectiveness of gITVs and raITVs. CTVs significantly differ from gITVs and raITVs in size (up to a factor 2 in volume). But also raITVs and gITVs differ significantly in size and are spatially displaced, particularly for lung patients. raITVs show a strong field dependence in shape. All volumes are reduced in size when gating is applied and considered during margin adaption. 4D dose distributions show big improvements when gITV or raITV are used compared to CTVs. However, the use of either gITVs or raITVs do not result in significant differences. If raITVs are used, slightly better target coverage is gained at the cost of more healthy tissue exposure. Our results emphasize that adapted target volumes have to be used for scanned particle therapy in the presence of motion. However, even though gITVs and raITVs differ significantly in shape and size, this difference does not necessarily translate into significant differences in the resultant 4D dose distributions. {\textcopyright} 2013 Institute of Physics and Engineering in Medicine.},
author = {Knopf, Antje Christin and Boye, Dirk and Lomax, Antony and Mori, Shininchiro},
doi = {10.1088/0031-9155/58/17/6079},
issn = {00319155},
journal = {Physics in Medicine and Biology},
number = {17},
pages = {6079--6094},
title = {{Adequate margin definition for scanned particle therapy in the incidence of intrafractional motion}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {58},
year = {2013}
}
@article{Kopecky2012,
abstract = {Three-dimensional (3D) reconstructions of the vertebrate inner ear have provided novel insights into the development of this complex organ. 3D reconstructions enable superior analysis of phenotypic differences between wild type and mutant ears but can result in laborious work when reconstructed from physically sectioned material. Although nondestructive optical sectioning light sheet microscopy may ultimately prove the ideal solution, these technologies are not yet commercially available, or in many instances are not monetarily feasible. Here we introduce a simple technique to image a fluorescently labelled ear at different stages throughout development at high resolution enabling 3D reconstruction of any component of the inner ear using confocal microscopy. We provide a step-by-step manual from tissue preparation to imaging to 3D reconstruction and analysis including a rationale and troubleshooting guide at each step for researchers with different equipment, protocols, and access to resources to successfully incorporate the principles of this method and customize them to their laboratory settings. {\textcopyright} 2012 Royal Microscopical Society.},
author = {Kopecky, B. J. and Duncan, J. S. and Elliott, K. L. and Fritzsch, B.},
doi = {10.1111/j.1365-2818.2012.03673.x},
issn = {00222720},
journal = {Journal of Microscopy},
keywords = {Confocal microscopy,Inner ear,Mouse,STSLIM,Segmentation,Three-dimensional reconstruction},
number = {3},
pages = {292--298},
title = {{Three-dimensional reconstructions from optical sections of thick mouse inner ears using confocal microscopy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {248},
year = {2012}
}
@article{Korez2015,
abstract = {Automated and semi-automated detection and segmentation of spinal and vertebral structures from computed tomography (CT) images is a challenging task due to a relatively high degree of anatomical complexity, presence of unclear boundaries and articulation of vertebrae with each other, as well as due to insufficient image spatial resolution, partial volume effects, presence of image artifacts, intensity variations and low signal-to-noise ratio. In this paper, we describe a novel framework for automated spine and vertebrae detection and segmentation from 3-D CT images. A novel optimization technique based on interpolation theory is applied to detect the location of the whole spine in the 3-D image and, using the obtained location of the whole spine, to further detect the location of individual vertebrae within the spinal column. The obtained vertebra detection results represent a robust and accurate initialization for the subsequent segmentation of individual vertebrae, which is performed by an improved shape-constrained deformable model approach. The framework was evaluated on two publicly available CT spine image databases of 50 lumbar and 170 thoracolumbar vertebrae. Quantitative comparison against corresponding reference vertebra segmentations yielded an overall mean centroid-to-centroid distance of 1.1 mm and Dice coefficient of 83.6{\%} for vertebra detection, and an overall mean symmetric surface distance of 0.3 mm and Dice coefficient of 94.6{\%} for vertebra segmentation. The results indicate that by applying the proposed automated detection and segmentation framework, vertebrae can be successfully detected and accurately segmented in 3-D from CT spine images.},
author = {Korez, Robert and Ibragimov, Bulat and Likar, Bostjan and Pernus, Franjo and Vrtovec, Tomaz},
doi = {10.1109/TMI.2015.2389334},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Computed tomography,deformable models,image segmentation,interpolation theory,object detection,spine,vertebra},
number = {8},
pages = {1649--1662},
title = {{A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {34},
year = {2015}
}
@article{Korfiatis2016,
abstract = {Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O6-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95{\%} CI: 0.780.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker.},
author = {Korfiatis, Panagiotis and Kline, Timothy L. and Coufalova, Lucie and Lachance, Daniel H. and Parney, Ian F. and Carter, Rickey E. and Buckner, Jan C. and Erickson, Bradley J.},
doi = {10.1118/1.4948668},
issn = {00942405},
journal = {Medical Physics},
keywords = {MGMT,MRI,glioblastoma multiforme,imaging biomarkers,random forest,support vector machines},
number = {6},
pages = {2835--2844},
title = {{MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2016}
}
@article{Kortesniemi2012,
abstract = {Background: Computed tomography (CT) has become the main contributor of the cumulative radiation exposure in radiology. Information on cumulative exposure history of the patient should be available for efficient management of radiation exposures and for radiological justification. Purpose: To develop and evaluate automatic image registration for organ dose calculation in CT. Material and Methods: Planning radiograph (scout) image data describing CT scan ranges from 15 thoracic CT examinations (9 men and 6 women) and 10 abdominal CT examinations (6 men and 4 women) were co-registered with the reference trunk CT scout image. 2-D affine transformation and normalized correlation metric was used for image registration. Longitudinal (z-axis) scan range coordinates on the reference scout image were converted into slice locations on the CT-Expo anthropomorphic male and female models, following organ and effective dose calculations. Results: The average deviation of z-location of studied patient images from the corresponding location in the reference scout image was 6.2 mm. The ranges of organ and effective doses with constant exposure parameters were from 0 to 28.0 mGy and from 7.3 to 14.5 mSv, respectively. The mean deviation of the doses for fully irradiated organs (inside the scan range), partially irradiated organs and non-irradiated organs (outside the scan range) was 1{\%}, 5{\%}, and 22{\%}, respectively, due to image registration. Conclusion: The automated image processing method to registrate individual chest and abdominal CT scout radiograph with the reference scout radiograph is feasible. It can be used to determine the individual scan range coordinates in z-direction to calculate the organ dose values. The presented method could be utilized in automatic organ dose calculation in CT for radiation exposure tracking of the patients.},
author = {Kortesniemi, Mika and Salli, Eero and Seuri, Raija},
doi = {10.1258/ar.2012.110611},
issn = {02841851},
journal = {Acta Radiologica},
keywords = {Computed tomography,Dosimetry,Technical aspects},
number = {8},
pages = {908--913},
title = {{Organ dose calculation in CT based on scout image data and automatic image registration}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {53},
year = {2012}
}
@article{Kostenko2018,
abstract = {We propose a combination of an experimental approach and a reconstruction technique that leads to reduction of artefacts in X-ray computer tomography of strongly attenuating objects. Through fully automatic data alignment, data generated in multiple experiments with varying object orientations are combined. Simulations and experiments show that the solutions computed using algebraic methods based on multiple acquisitions can achieve a dramatic improvement in the reconstruction quality, even when each acquisition generates a reduced number of projections. The approach does not require any advanced setup components making it ideal for laboratory-based X-ray tomography.},
author = {Kostenko, Alexander and Andriiashen, Vladyslav and Batenburg, Kees Joost},
doi = {10.1364/oe.26.028982},
issn = {1094-4087},
journal = {Optics Express},
number = {22},
pages = {28982},
title = {{Registration-based multi-orientation tomography}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {26},
year = {2018}
}
@article{Kremer2013,
abstract = {Ultrasound assessment of myocardial strain can provide valuable information on regional cardiac function. However, Doppler-based methods often used in practice for strain estimation suffer from angle dependency. In this study, a partial solution to that fundamental limitation is presented. We have previously reported using simulated data sets that spatial compounding of axial velocities obtained at three steering angles can theoretically outperform 2-D speckle tracking for 2-D strain estimation in the mouse heart. In this study, the feasibility of the method was analyzed invivo using spatial compounding of Doppler velocities on six mice with myocardial infarction and five controls, and results were compared with those of tagged microscopic magnetic resonance imaging ($\mu$MRI). Circumferential estimates quantified by means of both ultrasound and $\mu$MRI could detect regional dysfunction. Between echocardiography and $\mu$MRI, a good regression coefficient was obtained for circumferential strain estimates (. r= 0.69), whereas radial strain estimates correlated only moderately (. r= 0.37). A second echocardiography was performed after $\mu$MRI to test the reproducibility of the compounding method. This yielded a higher correlation coefficient for the circumferential component than for the radial component (. r= 0.74 circumferentially, r= 0.49 radially). {\textcopyright} 2013 World Federation for Ultrasound in Medicine {\&} Biology.},
author = {Kremer, Florence and Dresselaers, Tom and Heyde, Brecht and Ferferieva, Vesselina and Caluw{\'{e}}, Ellen and Choi, Hon Fai and Claus, Piet and Oosterlinck, Wouter and Janssens, Stefan and Himmelreich, Uwe and D'hooge, Jan},
doi = {10.1016/j.ultrasmedbio.2013.04.004},
issn = {1879291X},
journal = {Ultrasound in Medicine and Biology},
keywords = {2-D myocardial strain,Doppler,Mouse,Spatial compounding},
number = {10},
pages = {1848--1860},
title = {{2-D Strain Assessment in the Mouse Through Spatial Compounding of Myocardial Velocity Data: InVivo Feasibility}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84883465702{\&}doi=10.1016{\%}2Fj.ultrasmedbio.2013.04.004{\&}partnerID=40{\&}md5=7e4e566e8788b13da38aa8c29cc1271b},
volume = {39},
year = {2013}
}
@inproceedings{Kremer,
abstract = {Ultrasound assessment of myocardial strain can give valuable information on regional cardiac function. Speckle tracking is often used for this purpose as it can estimate the 2D myocardial strain tensor. However, in the mouse setting, speckle tracking remains challenging due to the high heart rate and the relatively thin wall compared to the typical size of the speckles. We have previously shown using simulated data sets that spatial compounding of axial velocities obtained at 3 steering angles can outperform 2D speckle tracking for 2D strain estimation in the mouse heart. In this study, beam steering was applied at -20°, 0° and 20° on short axis views of 5 control and 6 infarct mice. The lateral motion component was reconstructed through spatial compounding and results were compared to tagged $\mu$MRI. Circumferential estimates quantified by means of ultrasound and MRI could both detect regional dysfunction. Between echo and MRI, a good regression coefficient was obtained for circumferential strain estimates (r = 0.69), while radial strain estimates correlated only moderately (r = 0.37). {\textcopyright} 2011 IEEE.},
author = {Kremer, Florence and Dresselaers, Tom and Heyde, Brecht and Ferferieva, Vesselina and Caluw{\'{e}}, Ellen and Choi, Hon Fai and Oosterlinck, Wouter and Janssens, Stefan and Himmelreich, Uwe and D'hooge, Jan},
booktitle = {IEEE International Ultrasonics Symposium, IUS},
doi = {10.1109/ULTSYM.2011.0230},
isbn = {9781457712531},
issn = {19485719},
pages = {939--942},
title = {{2D myocardial strain in the mouse through spatial compounding: In-vivo feasibility study}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869030007{\&}doi=10.1109{\%}2FULTSYM.2011.0230{\&}partnerID=40{\&}md5=785bfbda4e1f62661f6a74a21c318fe6},
year = {2011}
}
@inproceedings{Kremera,
abstract = {In radiotherapy, fixed 2-D X-ray imaging devices have several advantages compared to gantry-mounted systems, such as less geometrical deformations and the possibility to monitor 3-D markers motion in real-time. However, there is a lack of studies concerning the geometry of these systems. For example, in the case of a non-orthogonal geometry, the effect of the angle between the X-ray axes has not been investigated yet. In the first part of this study, the optimal angle was analyzed theoretically. Results showed that 60° between the axes still enables displacements of the order of 0.35 mm to be detected. In a second step, the performance of the registration method for such oblique configuration was evaluated on phantom data sets. It was found that using images separated by 60° rather than 90° required more than twice as much the number of iterations to obtain sufficient accuracy (i.e. 0.7 mm and 0.5°). {\textcopyright} 2014 IEEE.},
author = {Kremer, Florence and Giard, Joachim and Sibomana, Merence and Seabra, Jose and {De Xivry}, Jonathan Orban and Labarbe, Rudi and MacQ, Benoit},
booktitle = {2014 1st International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2014},
doi = {10.1109/ATSIP.2014.6834600},
isbn = {9781479948888},
keywords = {patient positioning,radiotherapy,stereoscopic geometry},
pages = {172--176},
title = {{Feasibility and preliminary validation of 2D/3D image registration using fixed 2-D X-ray devices in image-guided radiotherapy}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903775128{\&}doi=10.1109{\%}2FATSIP.2014.6834600{\&}partnerID=40{\&}md5=a48a78e38dae1ddafa3b391003173fad},
year = {2014}
}
@incollection{Kuegler2018,
abstract = {Clinical outcome of several Minimally Invasive Surgeries (MIS) heavily depend on the accuracy of intraoperative pose estimation of the surgical instrument from intraoperative x-rays. The estimation consists of finding the tool in a given set of x-rays and extracting the necessary data to recreate the tool's pose for further navigation - resulting in severe consequences of incorrect estimation. Though state-of-the-art MIS literature has exploited image registration as a tool for instrument pose estimation, lack of practical considerations in previous study design render their conclusion ineffective from a clinical standpoint. One major issue of such a study is the lack of Ground Truth in clinical data -as there are no direct ways of measuring the ground truth pose and indirect estimation accumulates error. A systematic way to overcome this problem is to generate Digitally Reconstructed Radiographs (DRR), however, such procedure generates data which are free from measuring errors (e.g. noise, number of projections), resulting claims of registration performance inconclusive. Generalization of registration performance across different instruments with different Degrees of Freedom (DoF) has not been studied as well. By marrying a rigorous study design involving several clinical scenarios with, for example, several optimizers, metrics and others parameters for image registration, this paper bridges this gap effectively. Although the pose estimation error scales inversely with instrument size, we show image registration generalizes well for different instruments and DoF. In particular, it is shown that increasing the number of x-ray projections can reduce the pose estimation error significantly across instruments - which might lead to the acquisition of several x-rays for pose estimation in a clinical workflow.},
author = {K{\"{u}}gler, David and Jastrzebski, Martin Andrade and Mukhopadhyay, Anirban},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-92258-4_10},
isbn = {9783319922577},
issn = {16113349},
pages = {105--114},
title = {{Instrument pose estimation using registration for otobasis surgery}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048674870{\&}doi=10.1007{\%}2F978-3-319-92258-4{\_}10{\&}partnerID=40{\&}md5=e5ef96e3629df844b717127296fb9d5c},
volume = {10883 LNCS},
year = {2018}
}
@incollection{Kutarnia2012,
abstract = {We are developing a low cost ultrasound training system running on a laptop in which the user scans a generic 3D curved surface representing the patient using a 5 DoF sensor. A critical component of this system is the generation of ultrasound training image volumes, which need to cover a complete body region in order to provide a realistic scanning experience. This research attempts to develop stitching techniques to generate large global volumes from smaller overlapping volumes acquired using freehand techniques. {\textcopyright} 2012 The authors and IOS Press. All rights reserved.},
author = {Kutarnia, Jason and Pedersen, Peder C.},
booktitle = {Studies in Health Technology and Informatics},
doi = {10.3233/978-1-61499-022-2-238},
isbn = {9781614990215},
issn = {18798365},
keywords = {Freehand ultrasound,Non-rigid registration},
pages = {238--244},
title = {{Generation of 3D ultrasound training volumes from freehand acquired data}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84860628403{\&}doi=10.3233{\%}2F978-1-61499-022-2-238{\&}partnerID=40{\&}md5=1799c545e84fbed039abc026f951ab6d},
volume = {173},
year = {2012}
}
@article{Lakhman2017,
abstract = {Purpose: To investigate whether qualitative magnetic resonance (MR) features can distinguish leiomyosarcoma (LMS) from atypical leiomyoma (ALM) and assess the feasibility of texture analysis (TA). Methods: This retrospective study included 41 women (ALM = 22, LMS = 19) imaged with MRI prior to surgery. Two readers (R1, R2) evaluated each lesion for qualitative MR features. Associations between MR features and LMS were evaluated with Fisher's exact test. Accuracy measures were calculated for the four most significant features. TA was performed for 24 patients (ALM = 14, LMS = 10) with uniform imaging following lesion segmentation on axial T2-weighted images. Texture features were pre-selected using Wilcoxon signed-rank test with Bonferroni correction and analyzed with unsupervised clustering to separate LMS from ALM. Results: Four qualitative MR features most strongly associated with LMS were nodular borders, haemorrhage, “T2 dark” area(s), and central unenhanced area(s) (p ≤ 0.0001 each feature/reader). The highest sensitivity [1.00 (95{\%}CI:0.82-1.00)/0.95 (95{\%}CI: 0.74-1.00)] and specificity [0.95 (95{\%}CI:0.77-1.00)/1.00 (95{\%}CI:0.85-1.00)] were achieved for R1/R2, respectively, when a lesion had ≥3 of these four features. Sixteen texture features differed significantly between LMS and ALM (p-values: {\textless}0.001-0.036). Unsupervised clustering achieved accuracy of 0.75 (sensitivity: 0.70; specificity: 0.79). Conclusions: Combination of ≥3 qualitative MR features accurately distinguished LMS from ALM. TA was feasible. Key Points: • Four qualitative MR features demonstrated the strongest statistical association with LMS. • Combination of ≥3 these features could accurately differentiate LMS from ALM. • Texture analysis was a feasible semi-automated approach for lesion categorization.},
author = {Lakhman, Yulia and Veeraraghavan, Harini and Chaim, Joshua and Feier, Diana and Goldman, Debra A. and Moskowitz, Chaya S. and Nougaret, Stephanie and Sosa, Ramon E. and Vargas, Hebert Alberto and Soslow, Robert A. and Abu-Rustum, Nadeem R. and Hricak, Hedvig and Sala, Evis},
doi = {10.1007/s00330-016-4623-9},
issn = {14321084},
journal = {European Radiology},
keywords = {Atypical Uterine Leiomyoma,Magnetic Resonance Imaging,Texture Analysis,Uterine Leiomyoma,Uterine Leiomyosarcoma},
number = {7},
pages = {2903--2915},
title = {{Differentiation of Uterine Leiomyosarcoma from Atypical Leiomyoma: Diagnostic Accuracy of Qualitative MR Imaging Features and Feasibility of Texture Analysis}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {27},
year = {2017}
}
@article{Lan2016,
abstract = {Purpose: To investigate the incorporation of pretherapy regional ventilation function in predicting radiation fibrosis (RF) in stage III nonsmall cell lung cancer (NSCLC) patients treated with concurrent thoracic chemoradiotherapy. Methods: Thirty-seven patients with stage III NSCLC were retrospectively studied. Patients received one cycle of cisplatingemcitabine, followed by two to three cycles of cisplatinetoposide concurrently with involved-field thoracic radiotherapy (4666 Gy; 2 Gy/fraction). Pretherapy regional ventilation images of the lung were derived from 4D computed tomography via a density changebased algorithm with mass correction. In addition to the conventional dosevolume metrics (V20, V30, V40, and mean lung dose), dosefunction metrics (fV20, fV30, fV40, and functional mean lung dose) were generated by combining regional ventilation and radiation dose. A new class of metrics was derived and referred to as dosesubvolume metrics (sV20, sV30, sV40, and subvolume mean lung dose); these were defined as the conventional dosevolume metrics computed on the functional lung. Area under the receiver operating characteristic curve (AUC) values and logistic regression analyses were used to evaluate these metrics in predicting hallmark characteristics of RF (lung consolidation, volume loss, and airway dilation). Results: AUC values for the dosevolume metrics in predicting lung consolidation, volume loss, and airway dilation were 0.650.69, 0.570.70, and 0.690.76, respectively. The respective ranges for dosefunction metrics were 0.630.66, 0.610.71, and 0.720.80 and for dosesubvolume metrics were 0.500.65, 0.650.75, and 0.730.85. Using an AUC value = 0.70 as cutoff value suggested that at least one of each type of metrics (dosevolume, dosefunction, dosesubvolume) was predictive for volume loss and airway dilation, whereas lung consolidation cannot be accurately predicted by any of the metrics. Logistic regression analyses showed that dosefunction and dosesubvolume metrics were significant (P values ≤ 0.02) in predicting volume airway dilation. Likelihood ratio test showed that when combining dosefunction and/or dosesubvolume metrics with dosevolume metrics, the achieved improvements of prediction accuracy on volume loss and airway dilation were significant (P values ≤ 0.04). Conclusions: The authors results demonstrated that the inclusion of regional ventilation function improved accuracy in predicting RF. In particular, dosesubvolume metrics provided a promising method for preventing radiation-induced pulmonary complications.},
author = {Lan, Fujun and Jeudy, Jean and Senan, Suresh and {Van Sornsen De Koste}, J. R. and D'Souza, Warren and Tseng, Huan Hsin and Zhou, Jinghao and Zhang, Hao},
doi = {10.1118/1.4960367},
issn = {00942405},
journal = {Medical Physics},
keywords = {non-small cell lung cancer,radiation fibrosis,regional ventilation function},
number = {9},
pages = {5072--5079},
title = {{Should regional ventilation function be considered during radiation treatment planning to prevent radiation-induced complications?}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2016}
}
@article{Lang2013,
abstract = {Transcatheter aortic valve implantation (TAVI) is a minimally invasive alternative to conventional aortic valve replacement for severe aortic stenosis in high-risk patients in which a stent-based bioprosthetic valve is delivered into the heart via a catheter. TAVI relies largely on single-plane fluoroscopy for intraoperative navigation and guidance, which provides only gross imaging of anatomical structures. Inadequate imaging leading to suboptimal valve positioning contributes to many of the early complications experienced by TAVI patients, including valve embolism, coronary ostia obstruction, paravalvular leak, heart block, and secondary nephrotoxicity from excessive contrast use. Improved visualization can be provided using intraoperative registration of a CT-derived surface to transesophageal echo (TEE) images. In this study, the accuracy and robustness of a surface-based registration method suitable for intraoperative use are evaluated, and the performances of different TEE surface extraction methods are compared. The use of cross-plane TEE contours demonstrated the best accuracy, with registration errors of less than 5 mm. This guidance system uses minimal intraoperative interaction and workflow modification, does not require tool calibration or additional intraoperative hardware, and can be implemented at all cardiac centers at extremely low cost. {\textcopyright} 2013 IEEE.},
author = {Lang, Pencilla and Chu, Michael W.A. and Bainbridge, Dan and Guiraudon, Gerard M. and Jones, Douglas L. and Peters, Terry M.},
doi = {10.1109/TBME.2013.2249582},
issn = {00189294},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {CT,image registration,image-guided procedure,transcatheter aortic valve replacement,transesophageal echo},
number = {12},
pages = {3382--3390},
title = {{Surface-based CT-TEE registration of the aortic root}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {60},
year = {2013}
}
@article{Langer2011,
abstract = {The attractions of virtual computing are many: reduced costs, reduced resources and simplified maintenance. Any one of these would be compelling for a medical imaging professional attempting to support a complex practice on limited resources in an era of ever tightened reimbursement. In particular, the ability to run multiple operating systems optimized for different tasks (computational image processing on Linux versus office tasks on Microsoft operating systems) on a single physical machine is compelling. However, there are also potential drawbacks. High performance requirements need to be carefully considered if they are to be executed in an environment where the running software has to execute through multiple layers of device drivers before reaching the real disk or network interface. Our lab has attempted to gain insight into the impact of virtualization on performance by benchmarking the following metrics on both physical and virtual platforms: local memory and disk bandwidth, network bandwidth, and integer and floating point performance. The virtual performance metrics are compared to baseline performance on "bare metal." The results are complex, and indeed somewhat surprising. {\textcopyright} Society for Imaging Informatics in Medicine 2010.},
author = {Langer, Steve G. and French, Todd},
doi = {10.1007/s10278-010-9358-6},
issn = {08971889},
journal = {Journal of Digital Imaging},
keywords = {Computer hardware,Computer systems,Computers in medicine},
number = {5},
pages = {883--889},
title = {{Virtual machine performance benchmarking}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {24},
year = {2011}
}
@article{LaTorre2013,
abstract = {In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario-the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei. {\textcopyright} 2013 LaTorre, Alonso-Nanclares, Muelas, Pe{\~{n}}a and DeFelipe.},
author = {Latorre, Antonio and Alonso-Nanclares, Lidia and Muelas, Santiago and Pe{\~{n}}a, Jos{\'{e}} Mar{\'{i}}a and Defelipe, Javier},
doi = {10.3389/fnana.2013.00049},
issn = {16625129},
journal = {Frontiers in Neuroanatomy},
keywords = {3D reconstruction,Automatic segmentation,Cerebral cortex,Image processing,Neuron},
number = {DEC},
pages = {10},
title = {{3D segmentations of neuronal nuclei from confocal microscope image stacks}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {7},
year = {2013}
}
@article{LePoul2014,
abstract = {Genetic dominance in polymorphic loci may respond to selection; however, the evolution of dominance in complex traits remains a puzzle. We analyse dominance at a wing-patterning supergene controlling local mimicry polymorphism in the butterfly Heliconius numata. Supergene alleles are associated with chromosomal inversion polymorphism, defining ancestral versus derived alleles. Using controlled crosses and the new procedure, Colour Pattern Modelling, allowing whole-wing pattern comparisons, we estimate dominance coefficients between alleles. Here we show strict dominance in sympatry favouring mimicry and inconsistent dominance throughout the wing between alleles from distant populations. Furthermore, dominance among derived alleles is uncoordinated across wing-pattern elements, producing mosaic heterozygous patterns determined by a hierarchy in colour expression. By contrast, heterozygotes with an ancestral allele show complete, coordinated dominance of the derived allele, independently of colours. Therefore, distinct dominance mechanisms have evolved in association with supergene inversions, in response to strong selection on mimicry polymorphism.},
author = {{Le Poul}, Yann and Whibley, Annabel and Chouteau, Mathieu and Prunier, Florence and Llaurens, Violaine and Joron, Mathieu},
doi = {10.1038/ncomms6644},
issn = {20411723},
journal = {Nature Communications},
pages = {8},
title = {{Evolution of dominance mechanisms at a butterfly mimicry supergene}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {5},
year = {2014}
}
@article{Lebre2019,
abstract = {Background: Proper segmentation of the liver from medical images is critical for computer-assisted diagnosis, therapy and surgical planning. Knowledge of its vascular structure allows division of the liver into eight functionally independent segments, each with its own vascular inflow, known as the Couinaud scheme. Couinaud's description is the most widely used classification, since it is well-suited for surgery and accurate for the localization of lesions. However, automatic segmentation of the liver and its vascular structure to construct the Couinaud scheme remains a challenging task. Methods: We present a complete framework to obtain Couinaud's classification in three main steps; first, we propose a model-based liver segmentation, then a vascular segmentation based on a skeleton process, and finally, the construction of the eight independent liver segments. Our algorithms are automatic and allow 3D visualizations. Results: We validate these algorithms on various databases with different imaging modalities (Magnetic Resonance Imaging (MRI) and Computed Tomography (CT)). Experimental results are presented on diseased livers, which pose complex challenges because both the overall organ shape and the vessels can be severely deformed. A mean DICE score of 0.915 is obtained for the liver segmentation, and an average accuracy of 0.98 for the vascular network. Finally, we present an evaluation of our method for performing the Couinaud segmentation thanks to medical reports with promising results. Conclusions: We were able to automatically reconstruct 3-D volumes of the liver and its vessels on MRI and CT scans. Our goal is to develop an improved method to help radiologists with tumor localization.},
author = {Lebre, Marie Ange and Vacavant, Antoine and Grand-Brochier, Manuel and Rositi, Hugo and Abergel, Armand and Chabrot, Pascal and Magnin, Beno{\^{i}}t},
doi = {10.1016/j.compbiomed.2019.04.014},
issn = {18790534},
journal = {Computers in Biology and Medicine},
keywords = {CT and MRI volumes,Couinaud,Liver segmentation,Medical imaging,Vessel segmentation},
pages = {42--51},
title = {{Automatic segmentation methods for liver and hepatic vessels from CT and MRI volumes, applied to the Couinaud scheme}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {110},
year = {2019}
}
@article{Lebre2019a,
abstract = {Developing methods to segment the liver in medical images, study and analyze it remains a significant challenge. The shape of the liver can vary considerably from one patient to another, and adjacent organs are visualized in medical images with similar intensities, making the boundaries of the liver ambiguous. Consequently, automatic or semi-automatic segmentation of liver is a difficult task. Moreover, scanning systems and magnetic resonance imaging have different settings and parameters. Thus the images obtained differ from one machine to another. In this article, we propose an automatic model-based segmentation that allows building a faithful 3-D representation of the liver, with a mean Dice value equal to 90.3{\%} on CT and MRI datasets. We compare our algorithm with a semi-automatic method and with other approaches according to the state of the art. Our method works with different data sources, we use a large quantity of CT and MRI images from machines in various hospitals and multiple DICOM images available from public challenges. Finally, for evaluation of liver segmentation approaches in state of the art, robustness is not adequacy addressed with a precise definition. Another originality of this article is the introduction of a novel measure of robustness, which takes into account the liver variability at different scales.},
author = {Lebre, Marie Ange and Vacavant, Antoine and Grand-Brochier, Manuel and Rositi, Hugo and Strand, Robin and Rosier, Hubert and Abergel, Armand and Chabrot, Pascal and Magnin, Beno{\^{i}}t},
doi = {10.1016/j.compmedimag.2019.05.003},
issn = {18790771},
journal = {Computerized Medical Imaging and Graphics},
keywords = {3-D,Automatic segmentation,CT,Liver,MRI,Robustness,Shape model,Variability},
pages = {11},
title = {{A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {76},
year = {2019}
}
@article{Lee2017,
abstract = {Background Since there is no way to measure electric current generated by transcranial direct current stimulation (tDCS) inside the human head through in vivo experiments, numerical analysis based on the finite element method has been widely used to estimate the electric field inside the head. In 2013, we released a MATLAB toolbox named COMETS, which has been used by a number of groups and has helped researchers to gain insight into the electric field distribution during stimulation. The aim of this study was to develop an advanced MATLAB toolbox, named COMETS2, for the numerical analysis of the electric field generated by tDCS. New method COMETS2 can generate any sizes of rectangular pad electrodes on any positions on the scalp surface. To reduce the large computational burden when repeatedly testing multiple electrode locations and sizes, a new technique to decompose the global stiffness matrix was proposed. Results As examples of potential applications, we observed the effects of sizes and displacements of electrodes on the results of electric field analysis. The proposed mesh decomposition method significantly enhanced the overall computational efficiency. Comparison with existing methods We implemented an automatic electrode modeler for the first time, and proposed a new technique to enhance the computational efficiency. Conclusions In this paper, an efficient toolbox for tDCS analysis is introduced (freely available at http://www.cometstool.com). It is expected that COMETS2 will be a useful toolbox for researchers who want to benefit from the numerical analysis of electric fields generated by tDCS.},
author = {Lee, Chany and Jung, Young Jin and Lee, Sang Jun and Im, Chang Hwan},
doi = {10.1016/j.jneumeth.2016.12.008},
issn = {1872678X},
journal = {Journal of Neuroscience Methods},
keywords = {Electrostatic field,Finite element method (FEM),MATLAB toolbox,Neuromodulation,Transcranial direct current stimulation (tDCS)},
pages = {56--62},
title = {{COMETS2: An advanced MATLAB toolbox for the numerical analysis of electric fields generated by transcranial direct current stimulation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {277},
year = {2017}
}
@article{Lin2017,
abstract = {BACKGROUND AND PURPOSE: Clinical brain MR imaging registration algorithms are often made available by commercial vendors without figures of merit. The purpose of this study was to suggest a rational performance comparison methodology for these products. MATERIALS AND METHODS: Twenty patients were imaged on clinical 3T scanners by using 4 sequences: T2-weighted, FLAIR, susceptibility-weighted angiography, and T1 postcontrast. Fiducial landmark sites (n = 1175) were specified throughout these image volumes to define identical anatomic locations across sequences. Multiple registration algorithms were applied by using the T2 sequence as a fixed reference. Euclidean error was calculated before and after each registration and compared with a criterion standard landmark registration. The Euclidean effectiveness ratio is the fraction of Euclidean error remaining after registration, and the statistical effectiveness ratio is similar, but accounts for dispersion and noise. RESULTS: Before registration, error values for FLAIR susceptibility-weighted angiography, and T1 postcontrast were 2.07±0.55 mm, 2.63±0.62 mm, and 3.65±2.00 mm, respectively. Postregistration, the best error values for FLAIR, susceptibility-weighted angiography, and T1 postcontrast were 1.55±0.46 mm, 1.34±0.23 mm, and 1.06±0.16 mm, with Euclidean effectiveness ratio values of 0.493,0.181, and 0.096 and statistical effectiveness ratio values of 0.573, 0.352, and 0.929 for rigid mutual information, affine mutual information, and a commercial GE registration, respectively. CONCLUSIONS: We demonstrate a method for comparing the performance of registration algorithms and suggest the Euclidean error, Euclidean effectiveness ratio, and statistical effectiveness ratio as performance metrics for clinical registration algorithms. These figures of merit allow registration algorithms to be rationally compared.},
author = {Lin, J. S. and Fuentes, D. T. and Chandler, A. and Prabhu, S. S. and Weinberg, J. S. and Baladandayuthapani, V. and Hazle, J. D. and Schellingerhout, D.},
doi = {10.3174/ajnr.A5122},
issn = {1936959X},
journal = {American Journal of Neuroradiology},
number = {5},
pages = {973--980},
title = {{Performance assessment for brain MR imaging registration methods}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {38},
year = {2017}
}
@article{Liu2017,
abstract = {{\textcopyright} 2017 Optical Society of America. Multimodality molecular imaging emerges as a powerful strategy for correlating multimodal information. We developed a pentamodal imaging system which can perform positron emission tomography, bioluminescence tomography, fluorescence molecular tomography, Cerenkov luminescence tomography and X-ray computed tomography successively. Performance of sub-systems corresponding to different modalities were characterized. In vivo multimodal imaging of an orthotopic hepatocellular carcinoma xenograft mouse model was performed, and acquired multimodal images were fused. The feasibility of pentamodal tomographic imaging system was successfully validated with the imaging application on the mouse model. The ability of integrating anatomical, metabolic, and pharmacokinetic information promises applications of multimodality molecular imaging in precise medicine.},
author = {Liu, Muhan and Guo, Hongbo and Liu, Hongbo and Zhang, Zeyu and Chi, Chongwei and Hui, Hui and Dong, Di and Hu, Zhenhua and Tian, Jie},
doi = {10.1364/boe.8.001356},
issn = {2156-7085},
journal = {Biomedical Optics Express},
number = {3},
pages = {1356},
title = {{In vivo pentamodal tomographic imaging for small animals}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {8},
year = {2017}
}
@article{Liu2015,
abstract = {The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.},
author = {Liu, Sidong and Cai, Weidong and Liu, Siqi and Zhang, Fan and Fulham, Michael and Feng, Dagan and Pujol, Sonia and Kikinis, Ron},
doi = {10.1007/s40708-015-0020-4},
issn = {21984026},
journal = {Brain Informatics},
keywords = {Medical image computing,Multimodal,Neuroimaging},
number = {3},
pages = {181--195},
title = {{Multimodal neuroimaging computing: the workflows, methods, and platforms}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84977927257{\&}doi=10.1007{\%}2Fs40708-015-0020-4{\&}partnerID=40{\&}md5=5b2c05f1154939f3d1a0412038a423e1},
volume = {2},
year = {2015}
}
@article{Liu2014,
abstract = {The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy. {\textcopyright} 2014 Elsevier B.V.},
author = {Liu, Ting and Jones, Cory and Seyedhosseini, Mojtaba and Tasdizen, Tolga},
doi = {10.1016/j.jneumeth.2014.01.022},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
keywords = {Electron microscopy,Hierarchical segmentation,Image segmentation,Neuron reconstruction,Semi-automatic segmentation},
pages = {88--102},
title = {{A modular hierarchical approach to 3D electron microscopy image segmentation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {226},
year = {2014}
}
@article{Liu2014a,
abstract = {Personalized tumor growth model is valuable in tumor staging and therapy planning. In this paper, we present a patient specific tumor growth model based on longitudinal multimodal imaging data including dual-phase CT and FDG-PET. The proposed Reaction-Advection-Diffusion model is capable of integrating cancerous cell proliferation, infiltration, metabolic rate and extracellular matrix biomechanical response. To bridge the model with multimodal imaging data, we introduce Intracellular Volume Fraction (ICVF) measured from dual-phase CT and Standardized Uptake Value (SUV) measured from FDG-PET into the model. The patient specific model parameters are estimated by fitting the model to the observation, which leads to an inverse problem formalized as a coupled Partial Differential Equations (PDE)-constrained optimization problem. The optimality system is derived and solved by the Finite Difference Method. The model was evaluated by comparing the predicted tumors with the observed tumors in terms of average surface distance (ASD), root mean square difference (RMSD) of the ICVF map, average ICVF difference (AICVFD) of tumor surface and tumor relative volume difference (RVD) on six patients with pathologically confirmed pancreatic neuroendocrine tumors. The ASD between the predicted tumor and the reference tumor was 2.4 ± 0.5. mm, the RMSD was 4.3 ± 0.4{\%}, the AICVFD was 2.6 ± 0.6{\%}, and the RVD was 7.7 ± 1.3{\%}. {\textcopyright} 2014.},
author = {Liu, Yixun and Sadowski, Samira M. and Weisbrod, Allison B. and Kebebew, Electron and Summers, Ronald M. and Yao, Jianhua},
doi = {10.1016/j.media.2014.02.005},
issn = {13618423},
journal = {Medical Image Analysis},
keywords = {Intracellular volume fraction,Metabolic rate,Multimodal images,Tumor growth modeling},
number = {3},
pages = {555--566},
title = {{Patient specific tumor growth prediction using multimodal images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {18},
year = {2014}
}
@article{Lok2017,
abstract = {BACKGROUND: Brain tumor segmentation is a challenging task for its variation in intensity. The phenomenon is caused by the inhomogeneous content of tumor tissue and the choice of imaging modality. In 2010 Zhang developed the Selective Binary Gaussian Filtering Regularizing Level Set (SBGFRLS) model that combined the merits of edge-based and region-based segmentation.
OBJECTIVE: To improve the SBGFRLS method by modifying the singed pressure force (SPF) term with multiple image information and demonstrate effectiveness of proposed method on clinical images.
METHODS: In original SBGFRLS model, the contour evolution direction mainly depends on the SPF. By introducing a directional term in SPF, the metric could control the evolution direction. The SPF is altered by statistic values enclosed by the contour. This concept can be extended to jointly incorporate multiple image information. The new SPF term is expected to bring a solution for blur edge problem in brain tumor segmentation. The proposed method is validated with clinical images including pre- and post-contrast magnetic resonance images. The accuracy and robustness is compared with sensitivity, specificity, DICE similarity coefficient and Jaccard similarity index.
RESULTS: Experimental results show improvement

license_type: permissive

branch_name: refs/heads/master

extension: bib