text:
669dc0a00ab47368c672c},
volume = {220},
year = {2016}
}
@article{RN884,
abstract = {Lung cancer is still one of the most popular types of cancer worldwide. The analysis of consecutive timely spaced CT scans is a useful tool to analyze the lesion's malignant behavior during treatment or for the follow up of lesions undetermined as to its nature (malignant or benign). This paper aims to propose a method to obtain more detailed information about the lesion changes through complementary studies on the biological activity of pulmonary nodules. The methodology presented uses mixture of dynamic textures to cluster different tissues of lung lesions, in accordance with the density change over time, and to describe regional changes using similarity measures. The study was conducted on two chest computed tomography databases. The Public Lung Database (PLD), which has lesions that undergo evaluation for drug therapy and a private database of lung lesions of undetermined diagnosis. The lesions from the public database had areas with density variations in the range from 0.01{\%} to 110.53{\%}. Lesions from the private database showed regions with density variations from 0.11{\%} to 46.94{\%} range. The density change analysis proposed, that is done by regionally different locations, provides more detailed information about their change over time. Lesions considered volumetrically stable may contain locations that suffer noticeable changes, as well as lesions with large volumetric growth may not show a significant change in density.},
author = {Quintanilha, Darlan Bruno Pontes and Silva, Arist{\'{o}}fanes Corr{\^{e}}a and {De Paiva}, Anselmo Cardoso and Gattass, Marcelo},
doi = {10.1166/jctn.2018.7321},
issn = {15461963},
journal = {Journal of Computational and Theoretical Nanoscience},
keywords = {Dynamic texture mixture,Lung cancer,Medical image,Temporal analysis,Tissue change detection},
number = {6-7},
pages = {1839--1852},
title = {{Mixture of dynamic textures applied to temporal analysis of lung lesions}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057328183{\&}doi=10.1166{\%}2Fjctn.2018.7321{\&}partnerID=40{\&}md5=332bb09efa18d59dcf58b44028959bc7},
volume = {15},
year = {2018}
}
@article{RN917,
abstract = {Purpose The main focus of the current paper is the clinical implementation of a Monte Carlo based platform for treatment plan validation for Tomotherapy and Cyberknife, without adding additional tasks to the dosimetry department. Methods The Monte Carlo platform consists of C++ classes for the actual functionality and a web based GUI that allows accessing the system using a web browser. Calculations are based on BEAMnrc/DOSXYZnrc and/or GATE and are performed automatically after exporting the dicom data from the treatment planning system. For Cyberknife treatments of moving targets, the log files saved during the treatment (position of robot, internal fiducials and external markers) can be used in combination with the 4D planning CT to reconstruct the actually delivered dose. The Monte Carlo platform is also used for calculation on MRI images, using pseudo-CT conversion. Results For Tomotherapy treatments we obtain an excellent agreement (within 2{\%}) for almost all cases. However, we have been able to detect a problem regarding the CT Hounsfield units definition of the Toshiba Large Bore CT when using a large reconstruction diameter. For Cyberknife treatments we obtain an excellent agreement with the Monte Carlo algorithm of the treatment planning system. For some extreme cases, when treating small lung lesions in low density lung tissue, small differences are obtained due to the different cut-off energy of the secondary electrons. Conclusions A Monte Carlo based treatment plan validation tool has successfully been implemented in clinical routine and is used to systematically validate all Cyberknife and Tomotherapy plans.},
author = {Reynaert, N. and Demol, B. and Charoy, M. and Bouchoucha, S. and Crop, F. and Wagner, A. and Lacornerie, T. and Dubus, F. and Rault, E. and Comte, P. and Cayez, R. and Boydev, C. and Pasquier, D. and Mirabel, X. and Lartigau, E. and Sarrazin, T.},
doi = {10.1016/j.ejmp.2016.09.009},
issn = {1724191X},
journal = {Physica Medica},
keywords = {Delivered dose,Monte Carlo,QA,Treatment planning},
number = {10},
pages = {1225--1237},
title = {{Clinical implementation of a Monte Carlo based treatment plan QA platform for validation of Cyberknife and Tomotherapy treatments}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84992718263{\&}doi=10.1016{\%}2Fj.ejmp.2016.09.009{\&}partnerID=40{\&}md5=3de38bf62efd4bb22fcce5398852ce95},
volume = {32},
year = {2016}
}
@article{RN948,
abstract = {In the context of head and neck cancer (HNC) adaptive radiation therapy (ART), the two purposes of the study were to compare the performance of multiple deformable image registration (DIR) methods and to quantify their impact for dose accumulation, in healthy structures. Fifteen HNC patients had a planning computed tomography (CT0) and weekly CTs during the 7 weeks of intensity-modulated radiation therapy (IMRT). Ten DIR approaches using different registration methods (demons or B-spline free form deformation (FFD)), preprocessing, and similarity metrics were tested. Two observers identified 14 landmarks (LM) on each CT-scan to compute LM registration error. The cumulated doses estimated by each method were compared. The two most effective DIR methods were the demons and the FFD, with both the mutual information (MI) metric and the filtered CTs. The corresponding LM registration accuracy (precision) was 2.44 mm (1.30 mm) and 2.54 mm (1.33 mm), respectively. The corresponding LM estimated cumulated dose accuracy (dose precision) was 0.85 Gy (0.93 Gy) and 0.88 Gy (0.95 Gy), respectively. The mean uncertainty (difference between maximal and minimal dose considering all the 10 methods) to estimate the cumulated mean dose to the parotid gland (PG) was 4.03 Gy (SD = 2.27 Gy, range: 1.06-8.91 Gy).},
author = {Rigaud, Bastien and Simon, Antoine and Castelli, Jo{\"{e}}l and Gobeli, Maxime and {Ospina Arango}, Juan David and Cazoulat, Guillaume and Henry, Olivier and Haigron, Pascal and {De Crevoisier}, Renaud},
doi = {10.1155/2015/726268},
issn = {23146141},
journal = {BioMed Research International},
title = {{Evaluation of deformable image registration methods for dose monitoring in head and neck radiotherapy}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924164245{\&}doi=10.1155{\%}2F2015{\%}2F726268{\&}partnerID=40{\&}md5=d9d5d935280b872c6cac8a60ef5e6f65},
volume = {2015},
year = {2015}
}
@article{RN935,
abstract = {The deposits of fat on the surroundings of the heart are correlated to several health risk factors such as atherosclerosis, carotid stiffness, coronary artery calcification, atrial fibrillation and many others. These deposits vary unrelated to obesity, which reinforces its direct segmentation for further quantification. However, manual segmentation of these fats has not been widely deployed in clinical practice due to the required human workload and consequential high cost of physicians and technicians. In this work, we propose a unified method for an autonomous segmentation and quantification of two types of cardiac fats. The segmented fats are termed epicardial and mediastinal, and stand apart from each other by the pericardium. Much effort was devoted to achieve minimal user intervention. The proposed methodology mainly comprises registration and classification algorithms to perform the desired segmentation. We compare the performance of several classification algorithms on this task, including neural networks, probabilistic models and decision tree algorithms. Experimental results of the proposed methodology have shown that the mean accuracy regarding both epicardial and mediastinal fats is 98.5{\%} (99.5{\%} if the features are normalized), with a mean true positive rate of 98.0{\%}. In average, the Dice similarity index was equal to 97.6{\%}.},
author = {Rodrigues, O. and Morais, F. F.C. and Morais, N. A.O.S. and Conci, L. S. and Neto, L. V. and Conci, A.},
doi = {10.1016/j.cmpb.2015.09.017},
issn = {18727565},
journal = {Computer Methods and Programs in Biomedicine},
keywords = {Atlas,Classification,Computed tomography,Image registration,Machine learning,Segmentation},
pages = {109--128},
title = {{A novel approach for the automated segmentation and volume quantification of cardiac fats on computed tomography}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84951273755{\&}doi=10.1016{\%}2Fj.cmpb.2015.09.017{\&}partnerID=40{\&}md5=03ff84a656a2d758e69a4052ed2af8ee},
volume = {123},
year = {2016}
}
@inproceedings{RN863,
abstract = {According to the guidelines, patients with locally advanced colorectal cancer undergo neoadjuvant chemotherapy. However, response to therapy is reached only up to 30{\%} of cases. Therefore, it would be important to predict response to therapy before treatment. In this study, we demonstrated that the simultaneous optimization of feature subset and classifier parameters on different imaging datasets (T2w, DWI and PET) could improve classification performance. On a dataset of 51 patients (21 responders, 30 non responders), we obtained an accuracy of 90{\%}, 84{\%} and 76{\%} using three optimized SVM classifiers fed with selected features from PET, T2w and ADC images, respectively.},
author = {Rosati, S. and Gianfreda, C. M. and Balestra, G. and Giannini, V. and Mazzetti, S. and Regge, D.},
booktitle = {2018 IEEE Life Sciences Conference, LSC 2018},
doi = {10.1109/LSC.2018.8572194},
isbn = {9781538667095},
keywords = {Feature selection,Genetic algorithms,Rectal cancer,Response to chemoradiotherapy,SVM optimization},
pages = {65--68},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Radiomics to predict response to neoadjuvant chemotherapy in rectal cancer: Influence of simultaneous feature selection and classifier optimization}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060201982{\&}doi=10.1109{\%}2FLSC.2018.8572194{\&}partnerID=40{\&}md5=2339615f5270587ffef9440d34bd5ca9},
year = {2018}
}
@article{RN888,
abstract = {Mass spectrometry imaging (MSI) is a transformative imaging method that supports the untargeted, quantitative measurement of the chemical composition and spatial heterogeneity of complex samples with broad applications in life sciences, bioenergy, and health. While MSI data can be routinely collected, its broad application is currently limited by the lack of easily accessible analysis methods that can process data of the size, volume, diversity, and complexity generated by MSI experiments. The development and application of cutting-edge analytical methods is a core driver in MSI research for new scientific discoveries, medical diagnostics, and commercial-innovation. However, the lack of means to share, apply, and reproduce analyses hinders the broad application, validation, and use of novel MSI analysis methods. To address this central challenge, we introduce the Berkeley Analysis and Storage Toolkit (BASTet), a novel framework for shareable and reproducible data analysis that supports standardized data and analysis interfaces, integrated data storage, data provenance, workflow management, and a broad set of integrated tools. Based on BASTet, we describe the extension of the OpenMSI mass spectrometry imaging science gateway to enable web-based sharing, reuse, analysis, and visualization of data analyses and derived data products. We demonstrate the application of BASTet and OpenMSI in practice to identify and compare characteristic substructures in the mouse brain based on their chemical composition measured via MSI.},
author = {R{\"{u}}bel, Oliver and Bowen, Benjamin P.},
doi = {10.1109/TVCG.2017.2744479},
issn = {10772626},
journal = {IEEE Transactions on Visualization and Computer Graphics},
keywords = {Analysis Workflows,Data management,Data provenance,Data sharing,Mass spectrometry imaging,Visualization},
number = {1},
pages = {1025--1035},
title = {{BASTet: Shareable and Reproducible Analysis and Visualization of Mass Spectrometry Imaging Data via OpenMSI}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028711599{\&}doi=10.1109{\%}2FTVCG.2017.2744479{\&}partnerID=40{\&}md5=ed08c67415ad9ee80b1a99e0116908fa},
volume = {24},
year = {2018}
}
@article{RN869,
abstract = {Cerebral palsy (CP) is a common pediatric neurodevelopmental disorder, frequently resulting in motor and developmental deficits and often accompanied by cognitive impairments. A regular pathobiological hallmark of CP is oligodendrocyte maturation impairment resulting in white matter (WM) injury and reduced axonal myelination. Regeneration therapies based on cell replacement are currently limited, but neural precursor cells (NPCs), as cellular support for myelination, represent a promising regeneration strategy to treat CP, although the transplantation parameters (e.g., timing, dosage, mechanism) remain to be determined. We optimized a hemiplegic mouse model of neonatal hypoxia-ischemia that mirrors the pathobiological hallmarks of CP and transplanted NPCs into the corpus callosum (CC), a major white matter structure impacted in CP patients. The NPCs survived, engrafted, and differentiated morphologically in male and female mice. Histology and MRI showed repair of lesioned structures. Furthermore, electrophysiology revealed functional myelination of the CC (e.g., restoration of conduction velocity), while cylinder and CatWalk tests demonstrated motor recovery of the affected forelimb. Endogenous oligodendrocytes, recruited in the CC following transplantation of exogenous NPCs, are the principal actors in this recovery process. The lack of differentiation of the transplanted NPCs is consistent with enhanced recovery due to an indirect mechanism, such as a trophic and/or “bio-bridge�? support mediated by endogenous oligodendrocytes. Our work establishes that transplantation of NPCs represents a viable therapeutic strategy for CP treatment, and that the enhanced recovery is mediated by endogenous oligodendrocytes. This will further our understanding and contribute to the improvement of cellular therapeutic strategies.},
author = {Rumajogee, Prakasham and Altamentova, Svetlana and Li, Lijun and Li, Junyi and Wang, Jian and Kuurstra, Alan and Khazaei, Mohamad and Beldick, Stephanie and Menon, Ravi S. and {Van der Kooy}, Derek and Fehlings, Michael G.},
doi = {10.1523/ENEURO.0369-18.2018},
issn = {23732822},
journal = {eNeuro},
keywords = {Cerebral palsy,Hypoxic-ischemia,Myelination,Neural precursor cells,Oligodendrocytes,White matter injury},
number = {5},
title = {{Exogenous neural precursor cell transplantation results in structural and functional recovery in a hypoxic-ischemic hemiplegic mouse model}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057951115{\&}doi=10.1523{\%}2FENEURO.0369-18.2018{\&}partnerID=40{\&}md5=904cdf49f286dcf83962f118f9096771},
volume = {5},
year = {2018}
}
@article{RN846,
abstract = {Human age prediction is an interesting and applicable issue in different fields. It can be based on various criteria such as face image, DNA methylation, chest plate radiographs, knee radiographs, dental images and etc. Most of the age prediction researches have mainly been based on images. Since the image processing and Machine Learning (ML) techniques have grown up, the investigations were led to use them in age prediction problem. The implementations would be used in different fields, especially in medical applications. Brain Age Estimation (BAE) has attracted more attention in recent years and it would be so helpful in early diagnosis of some neurodegenerative diseases such as Alzheimer, Parkinson, Huntington, etc. BAE is performed on Magnetic Resonance Imaging (MRI) images to compute the brain ages. Studies based on brain MRI shows that there is a relation between accelerated aging and accelerated brain atrophy. This refers to the effects of neurodegenerative diseases on brain structure while making the whole of it older. This paper reviews and summarizes the main approaches for age prediction based on brain MRI images including preprocessing methods, useful tools used in different research works and the estimation algorithms. We categorize the BAE methods based on two factors, first the way of processing MRI images, which includes pixel-based, surface-based, or voxel-based methods and second, the generation of ML algorithms that includes traditional or Deep Learning (DL) methods. The modern techniques as DL methods help MRI based age prediction to get results that are more accurate. In recent years, more precise and statistical ML approaches have been utilized with the help of related tools for simplifying computations and getting accurate results. Pros and cons of each research and the challenges in each work are expressed and some guidelines and deliberations for future research are suggested.},
author = {Sajedi, Hedieh and Pardakhti, Nastaran},
doi = {10.1007/s10916-019-1401-7},
issn = {1573689X},
journal = {Journal of Medical Systems},
keywords = {Age prediction,BAE,Brain MRI,Brain age,Chronological age,Deep Learning,Image processing,Machine Learning},
number = {8},
pmid = {31297614},
title = {{Age Prediction Based on Brain MRI Image: A Survey}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068766678{\&}doi=10.1007{\%}2Fs10916-019-1401-7{\&}partnerID=40{\&}md5=5d6cacfa42ec287f82160fe89c70fe3a},
volume = {43},
year = {2019}
}
@article{RN790,
abstract = {Introduction Longitudinal magnetic resonance imaging (MRI) analysis has an important role in multiple sclerosis diagnosis and follow-up. The presence of new T2-w lesions on brain MRI scans is considered a prognostic and predictive biomarker for the disease. In this study, we propose a supervised approach for detecting new T2-w lesions using features from image intensities, subtraction values, and deformation fields (DF). Methods One year apart multi-channel brain MRI scans were obtained for 60 patients, 36 of them with new T2-w lesions. Images from both temporal points were preprocessed and co-registered. Afterwards, they were registered using multi-resolution affine registration, allowing their subtraction. In particular, the DFs between both images were computed with the Demons non-rigid registration algorithm. Afterwards, a logistic regression model was trained with features from image intensities, subtraction values, and DF operators. We evaluated the performance of the model following a leave-one-out cross-validation scheme. Results In terms of detection, we obtained a mean Dice similarity coefficient of 0.77 with a true-positive rate of 74.30{\%} and a false-positive detection rate of 11.86{\%}. In terms of segmentation, we obtained a mean Dice similarity coefficient of 0.56. The performance of our model was significantly higher than state-of-the-art methods. Conclusions The performance of the proposed method shows the benefits of using DF operators as features to train a supervised learning model. Compared to other methods, the proposed model decreases the number of false-positives while increasing the number of true-positives, which is relevant for clinical settings.},
author = {Salem, Mostafa and Cabezas, Mariano and Valverde, Sergi and Pareto, Deborah and Oliver, Arnau and Salvi, Joaquim and Rovira, {\`{A}}lex and Llad{\'{o}}, Xavier},
doi = {10.1016/j.nicl.2017.11.015},
issn = {22131582},
journal = {NeuroImage: Clinical},
keywords = {Automatic new lesion detection,Brain,MRI,Machine learning,Multiple sclerosis},
pages = {607--615},
title = {{A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {17},
year = {2018}
}
@article{RN892,
abstract = {Purpose: Estimating the biodistribution and the pharmacokinetics from time-sequence SPECT images on a per-voxel basis is useful for studying activity nonuniformity or computing absorbed dose distributions by convolution of voxel kernels or Monte-Carlo radiation transport. Current approaches are either region-based, thus assuming uniform activity within the region, or voxel-based but using the same fitting model for all voxels. Methods: We propose a voxel-based multimodel fitting method (VoMM) that estimates a fitting function for each voxel by automatically selecting the most appropriate model among a predetermined set with Akaike criteria. This approach can be used to compute the time integrated activity (TIA) for all voxels in the image. To control fitting optimization that may fail due to excessive image noise, an approximated version based on trapezoid integration, named restricted method, is also studied. From this comparison, the number of failed fittings within images was estimated and analyzed. Numerical experiments were used to quantify uncertainties and feasibility was demonstrated with real patient data. Results: Regarding numerical experiments, root mean square errors of TIA obtained with VoMM were similar to those obtained with bi-exponential fitting functions, and were lower ({\textless} 5{\%} vs. {\textgreater} 10{\%}) than with single model approaches that consider the same fitting function for all voxels. Failure rates were lower with VoMM and restricted approaches than with single-model methods. On real clinical data, VoMM was able to fit 90{\%} of the voxels and led to less failed fits than single-model approaches. On regions of interest (ROI) analysis, the difference between ROI-based and voxel-based TIA estimations was low, less than 4{\%}. However, the computation of the mean residence time exhibited larger differences, up to 25{\%}. Conclusions: The proposed voxel-based multimodel fitting method, VoMM, is feasible on patient data. VoMM leads organ-based TIA estimations similar to conventional ROI-based method. However, for pharmacokinetics analysis, studies of spatial heterogeneity or voxel-based absorbed dose assessment, VoMM could be used preferentially as it prevents model overfitting.},
author = {Sarrut, David and Halty, Adrien and Badel, Jean Noel and Ferrer, Ludovic and Bardi{\`{e}}s, Manuel},
doi = {10.1002/mp.12586},
issn = {00942405},
journal = {Medical Physics},
keywords = {SPECT,dosimetry,targeted radionuclide therapy,time activity curve,voxel-based},
number = {12},
pages = {6280--6288},
title = {{Voxel-based multimodel fitting method for modeling time activity curves in SPECT images:}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85037856250{\&}doi=10.1002{\%}2Fmp.12586{\&}partnerID=40{\&}md5=e15c5e0e2cbe8975290ae3d911a44b52},
volume = {44},
year = {2017}
}
@article{RN809,
abstract = {Background: Men suspected of having clinically significant prostate cancer (sPC) increasingly undergo prostate MRI. The potential of deep learning to provide diagnostic support for human interpretation requires further evaluation. Purpose: To compare the performance of clinical assessment to a deep learning system optimized for segmentation trained with T2-weighted and diffusion MRI in the task of detection and segmentation of lesions suspicious for sPC. Materials and Methods: In this retrospective study, T2-weighted and diffusion prostate MRI sequences from consecutive men examined with a single 3.0-T MRI system between 2015 and 2016 were manually segmented. Ground truth was provided by combined targeted and extended systematic MRI-transrectal US fusion biopsy, with sPC defined as International Society of Urological Pathology Gleason grade group greater than or equal to 2. By using split-sample validation, U-Net was internally validated on the training set (80{\%} of the data) through cross validation and subsequently externally validated on the test set (20{\%} of the data). U-Net-derived sPC probability maps were calibrated by matching sextant-based cross-validation performance to clinical performance of Prostate Imaging Reporting and Data System (PI-RADS). Performance of PI-RADS and U-Net were compared by using sensitivities, specificities, predictive values, and Dice coefficient. Results: A total of 312 men (median age, 64 years; interquartile range [IQR], 58-71 years) were evaluated. The training set consisted of 250 men (median age, 64 years; IQR, 58-71 years) and the test set of 62 men (median age, 64 years; IQR, 60-69 years). In the test set, PI-RADS cutoffs greater than or equal to 3 versus cutoffs greater than or equal to 4 on a per-patient basis had sensitivity of 96{\%} (25 of 26) versus 88{\%} (23 of 26) at specificity of 22{\%} (eight of 36) versus 50{\%} (18 of 36). U-Net at probability thresholds of greater than or equal to 0.22 versus greater than or equal to 0.33 had sensitivity of 96{\%} (25 of 26) versus 92{\%} (24 of 26) (both P . .99) with specificity of 31{\%} (11 of 36) versus 47{\%} (17 of 36) (both P . .99), not statistically different from PI-RADS. Dice coefficients were 0.89 for prostate and 0.35 for MRI lesion segmentation. In the test set, coincidence of PI-RADS greater than or equal to 4 with U-Net lesions improved the positive predictive value from 48{\%} (28 of 58) to 67{\%} (24 of 36) for U-Net probability thresholds greater than or equal to 0.33 (P = .01), while the negative predictive value remained unchanged (83{\%} [25 of 30] vs 83{\%} [43 of 52]; P . .99). Conclusion: U-Net trained with T2-weighted and diffusion MRI achieves similar performance to clinical Prostate Imaging Reporting and Data System assessment.},
author = {Schelb, Patrick and Kohl, Simon and Radtke, Jan Philipp and Wiesenfarth, Manuel and Kickingereder, Philipp and Bickelhaupt, Sebastian and Kuder, Tristan Anselm and Stenzinger, Albrecht and Hohenfellner, Markus and Schlemmer, Heinz Peter and Maier-Hein, Klaus H. and Bonekamp, David},
doi = {10.1148/radiol.2019190938},
issn = {15271315},
journal = {Radiology},
number = {3},
pages = {607--617},
title = {{Classification of cancer at prostate MRI: Deep Learning versus Clinical PI-RADS Assessment}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {293},
year = {2019}
}
@article{RN901,
abstract = {Objective: Focused ultrasound (FUS) is rapidly gaining clinical acceptance for several target tissues in the human body. Yet, treating liver targets is not clinically applied due to a high complexity of the procedure (noninvasiveness, target motion, complex anatomy, blood cooling effects, shielding by ribs, and limited image-based monitoring). To reduce the complexity, numerical FUS simulations can be utilized for both treatment planning and execution. These use-cases demand highly accurate and computationally efficient simulations. Methods: We propose a numerical method for the simulation of abdominal FUS treatments during respiratory motion of the organs and target. Especially, a novel approach is proposed to simulate the heating during motion by solving Pennes' bioheat equation in a computational reference space, i.e., the equation is mathematically transformed to the reference. The approach allows for motion discontinuities, e.g., the sliding of the liver along the abdominal wall. Results: Implementing the solver completely on the graphics processing unit and combining it with an atlas-based ultrasound simulation approach yields a simulation performance faster than real time (less than 50-s computing time for 100 s of treatment time) on a modern off-the-shelf laptop. The simulation method is incorporated into a treatment planning demonstration application that allows to simulate real patient cases including respiratory motion. Conclusion: The high performance of the presented simulation method opens the door to clinical applications. Significance: The methods bear the potential to enable the application of FUS for moving organs.},
author = {Schwenke, Michael and Georgii, Joachim and Preusser, Tobias},
doi = {10.1109/TBME.2016.2619741},
issn = {15582531},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {Abdominal organs treatment,Biomedical computing,High-intensity focused ultrasound,Medical simulation,Medical treatment,Numerical models,Numerical simulation,Respiratory motion,Simulation during motion,Therapeutic ultrasound,Thermal Ablation,Thermal analysis},
number = {7},
pages = {1455--1468},
title = {{Fast numerical simulation of focused ultrasound treatments during respiratory motion with discontinuous motion boundaries}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85026725476{\&}doi=10.1109{\%}2FTBME.2016.2619741{\&}partnerID=40{\&}md5=b0cf2a924d4dd6d672f984b75f9bf20c},
volume = {64},
year = {2017}
}
@article{RN781,
abstract = {Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible local registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coup{\'{e}} or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.},
author = {Sdika, Micha{\"{e}}l},
doi = {10.1118/1.4935946},
issn = {00942405},
journal = {Medical Physics},
keywords = {atlas based segmentation,machine learning,multiple atlas,support vector machine},
number = {12},
pages = {7169--7181},
title = {{Enhancing atlas based segmentation with multiclass linear classifiers}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {42},
year = {2015}
}
@article{RN800,
abstract = {The ability to monitor tumor motion without implanted markers can potentially enable broad access to more accurate and precise lung radiotherapy. A major challenge is that kilovoltage (kV) imaging based methods are rarely able to continuously track the tumor due to the inferior tumor visibility on 2D kV images. Another challenge is the estimation of 3D tumor position based on only 2D imaging information. The aim of this work is to address both challenges by proposing a Bayesian approach for markerless tumor tracking for the first time. The proposed approach adopts the framework of the extended Kalman filter, which combines a prediction and measurement steps to make the optimal tumor position update. For each imaging frame, the tumor position is first predicted by a respiratory-correlated model. The 2D tumor position on the kV image is then measured by template matching. Finally, the prediction and 2D measurement are combined based on the 3D distribution of tumor positions in the past 10 s and the estimated uncertainty of template matching. To investigate the clinical feasibility of the proposed method, a total of 13 lung cancer patient datasets were used for retrospective validation, including 11 cone-beam CT scan pairs and two stereotactic ablative body radiotherapy cases. The ground truths for tumor motion were generated from the the 3D trajectories of implanted markers or beacons. The mean, standard deviation, and 95th percentile of the 3D tracking error were found to range from 1.6-2.9 mm, 0.6-1.5 mm, and 2.6-5.8 mm, respectively. Markerless tumor tracking always resulted in smaller errors compared to the standard of care. The improvement was the most pronounced in the superior-inferior (SI) direction, with up to 9.5 mm reduction in the 95th-percentile SI error for patients with {\textgreater}10 mm 5th-to-95th percentile SI tumor motion. The percentage of errors with 3D magnitude {\textless}5 mm was 96.5{\%} for markerless tumor tracking and 84.1{\%} for the standard of care. The feasibility of 3D markerless tumor tracking has been demonstrated on realistic clinical scenarios for the first time. The clinical implementation of the proposed method will enable more accurate and precise lung radiotherapy using existing hardware and workflow. Future work is focused on the clinical and real-time implementation of this method.},
author = {Shieh, Chun Chien and Caillet, Vincent and Dunbar, Michelle and Keall, Paul J. and Booth, Jeremy T. and Hardcastle, Nicholas and Haddad, Carol and Eade, Thomas and Feain, Ilana},
doi = {10.1088/1361-6560/aa6393},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {Bayesian,intrafraction imaging,lung cancer,markerless,tumor tracking},
number = {8},
pages = {3065--3080},
title = {{A Bayesian approach for three-dimensional markerless tumor tracking using kV imaging during lung radiotherapy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {62},
year = {2017}
}
@article{RN962,
abstract = {The ability to monitor tumor motion without implanted markers is clinically advantageous for lung image-guided radiotherapy (IGRT). Existing markerless tracking methods often suffer from overlapping structures and low visibility of tumors on kV projection images. We introduce the short arc tumor tracking (SATT) method to overcome these issues. The proposed method utilizes multiple kV projection images selected from a nine-degree imaging arc to improve tumor localization, and respiratory-correlated 4D cone-beam CT (CBCT) prior knowledge to minimize the effects of overlapping anatomies. The 3D tumor position is solved as an optimization problem with prior knowledge incorporated via regularization. We retrospectively validated SATT on 11 clinical scans from four patients with central tumors. These patients represent challenging scenarios for markerless tumor tracking due to the inferior adjacent contrast. The 3D trajectories of implanted fiducial markers were used as the ground truth for tracking accuracy evaluation. In all cases, the tumors were successfully tracked at all gantry angles. Compared to standard pre-treatment CBCT guidance alone, trajectory errors were significantly smaller with tracking in all cases, and the improvements were the most prominent in the superior-inferior direction. The mean 3D tracking error ranged from 2.2-9.9 mm, which was 0.4-2.6 mm smaller compared to pre-treatment CBCT. In conclusion, we were able to directly track tumors with inferior visibility on kV projection images using SATT. Tumor localization accuracies are significantly better with tracking compared to the current standard of care of lung IGRT. Future work involves the prospective evaluation and clinical implementation of SATT.},
author = {Shieh, Chun Chien and Keall, Paul J. and Kuncic, Zdenka and Huang, Chen Yu and Feain, Ilana},
doi = {10.1088/0031-9155/60/24/9437},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {IGRT,lung cancer,markerless,tumor tracking},
number = {24},
pages = {9437--9454},
title = {{Markerless tumor tracking using short kilovoltage imaging arcs for lung image-guided radiotherapy}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957922433{\&}doi=10.1088{\%}2F0031-9155{\%}2F60{\%}2F24{\%}2F9437{\&}partnerID=40{\&}md5=b33e8bc47270bdc2d818b5af2cf039d3},
volume = {60},
year = {2015}
}
@article{RN787,
abstract = {X-ray imaging is the conventional method for diagnosing the orthopedic condition of a patient. Computerized Tomography(CT) scanning is another diagnostic method that provides patient's 3D anatomical information. However, both methods have limitations when diagnosing the whole leg; X-ray imaging does not provide 3D information, and normal CT scanning cannot be performed with a standing posture. Obtaining 3D data regarding the whole leg in a standing posture is clinically important because it enables 3D analysis in the weight bearing condition. Based on these clinical needs, a hardware-based bi-plane X-ray imaging system has been developed; it uses two orthogonal X-ray images. However, such methods have not been made available in general clinics because of the hight cost. Therefore, we proposed a widely adaptive method for 2D X-ray image and 3D CT scan data. By this method, it is possible to threedimensionally analyze the whole leg in standing posture. The optimal position that generates the most similar image is the captured X-ray image. The algorithm verifies the similarity using the performance of the proposed method by simulation-based experiments. Then, we analyzed the internal-external rotation angle of the femur using real patient data. Approximately 10.55 degrees of internal rotations were found relative to the defined anterior-posterior direction. In this paper, we present a useful registration method using the conventional X-ray image and 3D CT scan data to analyze the whole leg in the weight-bearing condition.},
author = {Shim, Eungjune and Kim, Youngjun and Lee, Deukhee and Lee, Byung Hoon and Woo, Sungkyung and Lee, Kunwoo},
doi = {10.1007/s11766-018-3459-2},
issn = {10051031},
journal = {Applied Mathematics},
keywords = {2D-3D registration,3D analysis,CT,X-ray,simulated annealing},
number = {1},
pages = {59--70},
title = {{2D-3D registration for 3D analysis of lower limb alignment in a weight-bearing condition}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {33},
year = {2018}
}
@article{RN854,
abstract = {Hybrid 3D scaffolds composed of different biomaterials with fibrous structure or enriched with different inclusions (i.e., nano- and microparticles) have already demonstrated their positive effect on cell integration and regeneration. The analysis of fibers in hybrid biomaterials, especially in a 3D space is often difficult due to their various diameters (from micro to nanoscale) and compositions. Though biomaterials processing workflows are implemented, there are no software tools for fiber analysis that can be easily integrated into such workflows. Due to the demand for reproducible science with Jupyter notebooks and the broad use of the Python programming language, we have developed the new Python package quanfima offering a complete analysis of hybrid biomaterials, that include the determination of fiber orientation, fiber and/or particle diameter and porosity. Here, we evaluate the provided tensor-based approach on a range of generated datasets under various noise conditions. Also, we show its application to the X-ray tomography datasets of polycaprolactone fibrous scaffolds pure and containing silicate-substituted hydroxyapatite microparticles, hydrogels enriched with bioglass contained strontium and alpha-tricalcium phosphate microparticles for bone tissue engineering and porous cryogel 3D scaffold for pancreatic cell culturing. The results obtained with the help of the developed package demonstrated high accuracy and performance of orientation, fibers and microparticles diameter and porosity analysis.},
author = {Shkarin, Roman and Shkarin, Andrei and Shkarina, Svetlana and Cecilia, Angelica and Surmenev, Roman A. and Surmeneva, Maria A. and Weinhardt, Venera and Baumbach, Tilo and Mikut, Ralf},
doi = {10.1371/journal.pone.0215137},
issn = {19326203},
journal = {PLoS ONE},
number = {4},
title = {{Quanfima: An open source Python package for automated fiber analysis of biomaterials}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064242516{\&}doi=10.1371{\%}2Fjournal.pone.0215137{\&}partnerID=40{\&}md5=4418e4939d68c3c8368a16476e37b7fb},
volume = {14},
year = {2019}
}
@article{RN795,
abstract = {Deformable image registration (DIR) is a rapidly developing discipline in the field of medical imaging that has found numerous applications in modern radiation therapy. To be used in the clinical environment, DIR requires an accurate and robust algorithm supported by the careful evaluation. The purpose of this study was to evaluate the performance of the non-linear Dense Anatomical Block Matching (DABM) algorithm for CT-CBCT image registration of prostate cancer patients. Pre-treatment CT (pCT) images of five prostate patients that underwent intensity modulated radiation therapy (IMRT) were selected for this work. Mid-treatment CBCT data sets acquired during radiotherapy course were used to help validate the algorithm performance and benchmark against other commonly used DIR algorithms. Rigid alignment was followed by the DIR of considered images. After registration, structures (PTV, GTV, Bladder and Rectum) delineated on the pCT were deformed using the obtained deformation vector fields (DVFs), then propagated to the CBCT images and compared to the analogous contours delineated on the CBCT by an experienced radiation oncologist. The accuracy of image registration was assessed by several quantitative metrics: Dice Similarity Coefficient (DSC), Hausdorff Distances (HD; average and 95th percentile), Center of the Mass Shift (COM) as well as by physician validation. The topology of the obtained deformation vector fields was analyzed by the Jacobian determinant. The accuracy of the inverted DFVs was investigated by the application of the Inverse Consistency Error (ICE). The performance of the DABM algorithm was quantitatively compared to Rigid, Affine and B-spline algorithms. Results indicate that for all the patients and anatomical structures considered here, both the accuracy and the consistency of the DABM algorithm are considerably better than the other evaluated registration methods. Generated DVFs have a well-preserved topology and small ICEs. Presented findings show that DABM is a promising alternative to the existing common strategies for CT-CBCT image registration and its application in the adaptive radiation therapy of the pelvic region.},
author = {Siciarz, Pawel and McCurdy, Boyd and Alshafa, Faiez and Greer, Peter and Hatton, Joan and Wright, Philip},
doi = {10.1088/2057-1976/aacada},
issn = {20571976},
journal = {Biomedical Physics and Engineering Express},
keywords = {CT-CBCT deformable image registration,dense anatomical block matching,prostate cancer,radiation therapy},
number = {4},
pages = {15},
title = {{Evaluation of CT to CBCT non-linear dense anatomical block matching registration for prostate patients}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {4},
year = {2018}
}
@article{RN934,
abstract = {The authors proposed a solution to the over-segmentation of color images processed by watershed segmentation algorithm. The solution utilizes hierarchical cluster analysis and treats watersheds as objects characterized by a number of attributes. This paper briefly discusses the solution (clustering methods, their parameters, selected watershed attributes) and presents an algorithm used for selecting optimal parameters for cluster analysis. Detailed results obtained for a set of test images are presented and discussed.},
author = {Smo{\l}ka, Jakub and Skublewska-Paszkowska, Maria and {\L}ukasik, Edyta},
doi = {10.15199/48.2016.09.61},
issn = {00332097},
journal = {Przeglad Elektrotechniczny},
keywords = {Cluster analysis,Over-segmentation reduction,Watershed transformation},
number = {9},
pages = {250--256},
title = {{Algorytm doboru optymalnych parametr{\'{o}}w analizy skupie{\'{n}} zastosowanej do redukcji nadsegmentacji}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986000629{\&}doi=10.15199{\%}2F48.2016.09.61{\&}partnerID=40{\&}md5=311e10c8007ae785c0cef57929b23170},
volume = {92},
year = {2016}
}
@article{RN910,
abstract = {The paper presents efficient parallel computations of the patient-specific aortic valve on virtualized resources of the OpenStack cloud infrastructure. The main focus is on parallel performance analysis of the developed software service based on ANSYS Fluent platform, which runs on Docker containers of the private university cloud. The patient-specific aortic valve simulation, described by incompressible Navier-Stokes equations, is considered as a pilot application of the hosted cloud infrastructure. The parallel performance of the developed software service is assessed measuring parallel speedup of computations carried out on virtualized resources. The results obtained on Docker containers are compared with the performance measured by using the native hardware.},
author = {Staskuniene, M. and Kaceniauskas, A. and Starikovicius, V. and Maknickas, A. and Stupak, E. and Pacevic, R.},
doi = {10.4203/ccp.111.16},
issn = {17593433},
journal = {Civil-Comp Proceedings},
keywords = {ANSYS Fluent,Cloud computing,Docker,OpenStack,Patient-specific aortic valve simulation,Performance analysis},
title = {{Parallel simulation of the aortic valve flows on the openstack cloud}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020416209{\&}partnerID=40{\&}md5=c98d54cb21401e2192b0bf6051b51636},
volume = {111},
year = {2017}
}
@inproceedings{RN864,
abstract = {Medical imaging visualization technology takes a significant role in the medical community. With the assistance of medical imaging visualization applications, huge convenience has been brought into clinical diagnosis, monitoring, and treatment. It allows doctors and researchers to see inside the human body, to identify medical problems, and to diagnose diseases. This article presents a lightweight, fast and user-friendly image viewer for medical imaging called SPIFFY. Some developing methodologies with the integration of VTK, ITK, and Qt will be presented in this article. Besides, the minimalist user interface(UI) design of SPIFFY with an application of Human-Computer Interaction(HCI) psychology principles will also be introduced. Moreover, this article will identify the benefits provided by SPIFFY and present a benchmark against some existing medical visualization applications. Experiments using cognitive walkthrough evaluation shows that SPIFFY provides both high effectiveness and efficiency.},
author = {Sun, Jiayu and Chandra, Shekhar S.},
booktitle = {Proceedings of 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference, ITOEC 2018},
doi = {10.1109/ITOEC.2018.8740656},
editor = {Xu, B},
isbn = {9781538653739},
keywords = {Cognitive walkthrough evaluation,Human-computer interaction,Medical imaging,Visualizing software},
pages = {297--301},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{SPIFFY: A simpler image viewer for medical imaging}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068362296{\&}doi=10.1109{\%}2FITOEC.2018.8740656{\&}partnerID=40{\&}md5=1c3ff161aa23226e508668105edd9d2f},
year = {2018}
}
@incollection{RN880,
abstract = {The main aim of this work was to perform rigid registration of Computed Tomography (CT) and scanner datasets. The surgeon applies CT and scanner datasets in computer aided surgery and performs registration in order to visualize the location of surgical instrument on screen. It is well known fact that the registration procedure is crucial for efficient computer aiding of surgery. Selected algorithm should take into account types of datasets, required accuracy and time of calculations. The algorithms are classified basing on the various criteria: e.g. precision (coarse and fine registration), types of pointset (set of pair of corresponding points – so called point-point method, unorganized sets of points – so called surface registration). The paper presents exemplary results of applying the following algorithms: Landmark Transform (point-point registration), two methods of uninitialized Iterative Closest Point type (surface registration) and a hybrid method. The evaluated factors were: distance error (mean, minimal and maximal value) and running time of algorithm. The algorithms were tested on various datasets: (1) two similar datasets from Computed Tomography (one is geometrically transformed), (2) Computed Tomography dataset and cloud of points recorded using 3D Artec Space Spider scanner. In the first case the mean error values equaled: 102.08 mm – 121.70 mm for uninitialized ICPs methods, 0.005 mm for Landmark Transform method, and 0.0003 mm for hybrid method. The slowest algorithms in our tests were ICPs methods, faster was hybrid algorithm, and the fastest was Landmark Transform method. In the second case the distance errors were evaluated in four selected points, and the smallest errors were: 23.21 mm for uninitialized ICPs method, 0.69 mm for Landmark Transform, 9.03 for hybrid method. All algorithms were relatively slow for these large datasets, the fastest was Landmark Transform. In the second part of research we analysed the Target Registration Error (TRE) for fused Computed Tomography and scanner-recorded dataset. The TRE values equaled 0.7 mm - 2.8 mm. The results of CT – scanner datasets registration highly depend on the similarity of sets, especially their overlapping, but also their resolutions and uniformities.},
author = {{\'{S}}wi{\c{a}}tek-Najwer, Ewelina and {\.{Z}}uk, Magdalena and Majak, Marcin and Popek, Micha{\l}},
booktitle = {Lecture Notes in Computational Vision and Biomechanics},
doi = {10.1007/978-3-319-68195-5_38},
isbn = {22129391 (ISSN)},
issn = {22129413},
pages = {345--353},
publisher = {Springer Netherlands},
title = {{The rigid registration of CT and scanner dataset for computer aided surgery}},
type = {Serial},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032361988{\&}doi=10.1007{\%}2F978-3-319-68195-5{\_}38{\&}partnerID=40{\&}md5=f66e1b53e39dbae26c9578f6ea426d78},
volume = {27},
year = {2018}
}
@article{RN889,
abstract = {FIB/SEM nanotomography (FIB-nt) is a powerful technique for the determination and quantification of the three-dimensional microstructure in subsurface features. Often times, the microstructure of a sample is the ultimate determiner of the overall performance of a system, and a detailed understanding of its properties is crucial in advancing the materials engineering of a resulting device. While the FIB-nt technique has developed significantly in the 15 years since its introduction, advanced nanotomographic analysis is still far from routine, and a number of challenges remain in data acquisition and post-processing. In this work, we present a number of techniques to improve the quality of the acquired data, together with easy-to-implement methods to obtain “advanced” microstructural quantifications. The techniques are applied to a solid oxide fuel cell cathode of interest to the electrochemistry community, but the methodologies are easily adaptable to a wide range of material systems. Finally, results from an analyzed sample are presented as a practical example of how these techniques can be implemented.},
author = {Taillon, Joshua A. and Pellegrinelli, Christopher and Huang, Yi Lin and Wachsman, Eric D. and Salamanca-Riba, Lourdes G.},
doi = {10.1016/j.ultramic.2017.07.017},
issn = {18792723},
journal = {Ultramicroscopy},
keywords = {3D reconstruction,Focused ion beam,Microstructure quantification,Scanning electron microscopy,Tortuosity,Triple phase boundaries},
pages = {24--38},
title = {{Improving microstructural quantification in FIB/SEM nanotomography}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85027830512{\&}doi=10.1016{\%}2Fj.ultramic.2017.07.017{\&}partnerID=40{\&}md5=ec5c5bcb14a5795c82946322610e647d},
volume = {184},
year = {2018}
}
@inproceedings{RN928,
author = {Tan, Maxine and Li, Zheng and Moore, Kathleen and Thai, Theresa and Ding, Kai and Liu, Hong and Zheng, Bin},
booktitle = {Medical Imaging 2016: Computer-Aided Diagnosis},
doi = {10.1117/12.2216303},
editor = {Tourassi, G D and Armato, S G},
isbn = {9781510600201},
issn = {16057422},
pages = {97853D},
publisher = {SPIE},
title = {{A B-spline image registration based CAD scheme to evaluate drug treatment response of ovarian cancer patients}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988892588{\&}doi=10.1117{\%}2F12.2216303{\&}partnerID=40{\&}md5=eab17b988f48475720c13a6e6b359caa},
volume = {9785},
year = {2016}
}
@article{RN932,
abstract = {Although Response Evaluation Criteria in Solid Tumors (RECIST) is the current clinical guideline to assess size change of solid tumors after therapeutic treatment, it has a relatively lower association to the clinical outcome of progression free survival (PFS) of the patients. In this paper, we presented a new approach to assess responses of ovarian cancer patients to new chemotherapy drugs in clinical trials. We first developed and applied a multi-resolution B-spline based deformable image registration method to register two sets of computed tomography (CT) image data acquired pre- and post-treatment. The B-spline difference maps generated from the co-registered CT images highlight the regions related to the volumetric growth or shrinkage of the metastatic tumors, and density changes related to variation of necrosis inside the solid tumors. Using a testing dataset involving 19 ovarian cancer patients, we compared patients' response to the treatment using the new image registration method and RECIST guideline. The results demonstrated that using the image registration method yielded higher association with the six-month PFS outcomes of the patients than using RECIST. The image registration results also provided a solid foundation of developing new computerized quantitative image feature analysis schemes in the future studies.},
author = {Tan, Maxine and Li, Zheng and Qiu, Yuchen and McMeekin, Scott D. and Thai, Theresa C. and Ding, Kai and Moore, Kathleen N. and Liu, Hong and Zheng, Bin},
doi = {10.1109/TMI.2015.2473823},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Computed tomography (CT),Computer-aided diagnosis (CAD),Deformable image registration,Metastatic tumors,Ovarian cancer,Response evaluation criteria in solid tumors (RECI,Tumor volume and necrosis tracking},
number = {1},
pages = {316--325},
title = {{A new approach to evaluate drug treatment response of ovarian cancer patients based on deformable image registration}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959364855{\&}doi=10.1109{\%}2FTMI.2015.2473823{\&}partnerID=40{\&}md5=f4cd96ce6d440ec0f4576be7ed0f2e7c},
volume = {35},
year = {2016}
}
@article{RN933,
abstract = {{\textcopyright} 2016 American Vacuum Society. In order to utilize complementary imaging techniques to supply higher resolution data for fusion with secondary ion mass spectrometry (SIMS) chemical images, there are a number of aspects that, if not given proper consideration, could produce results which are easy to misinterpret. One of the most critical aspects is that the two input images must be of the same exact analysis area. With the desire to explore new higher resolution data sources that exists outside of the mass spectrometer, this requirement becomes even more important. To ensure that two input images are of the same region, an implementation of the insight segmentation and registration toolkit (ITK) was developed to act as a preprocessing step before performing image fusion. This implementation of ITK allows for several degrees of movement between two input images to be accounted for, including translation, rotation, and scale transforms. First, the implementation was confirmed to accurately register two multimodal images by supplying a known transform. Once validated, two model systems, a copper mesh grid and a group of RAW 264.7 cells, were used to demonstrate the use of the ITK implementation to register a SIMS image with a microscopy image for the purpose of performing image fusion.},
author = {Tarolli, Jay Gage and Bloom, Anna and Winograd, Nicholas},
doi = {10.1116/1.4939892},
issn = {1934-8630},
journal = {Biointerphases},
number = {2},
pages = {02A311},
title = {{Multimodal image fusion with SIMS: Preprocessing with image registration}},
type = {Journal Article},
volume = {11},
year = {2016}
}
@article{RN899,
abstract = {Region of interest (ROI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from CAT scanners as pixel or voxel data. Previously, we presented a 2D method for curve-to-pixel registration. This paper contributes (i) a general mesh-to-raster (M2R) framework to register ROIs in multi-modal images; (ii) a 3D surface-to-voxel application, and (iii) a comprehensive quantitative evaluation in 2D using ground truth provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem where the objective consists of a data term, which involves the signed distance function of the ROI from the reference image, and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The ROI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit (ITK) and Elastix.},
archivePrefix = {arXiv},
arxivId = {1703.01972},
author = {Tatano, Rosalia and Berkels, Benjamin and Deserno, Thomas M.},
doi = {10.1117/1.jmi.4.4.044002},
eprint = {1703.01972},
issn = {2329-4310},
journal = {Journal of Medical Imaging},
number = {04},
pages = {1},
title = {{Mesh-to-raster region-of-interest-based nonrigid registration of multimodal images}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032914246{\&}doi=10.1117{\%}2F1.JMI.4.4.044002{\&}partnerID=40{\&}md5=c806ed79840aa4e089be7a24062199ec},
volume = {4},
year = {2017}
}
@article{RN839,
abstract = {Purpose: For planning and guidance of minimally invasive mitral valve repair procedures, 3D+t transesophageal echocardiography (TEE) sequences are acquired before and after the intervention. The valve is then visually and quantitatively assessed in selected phases. To enable a quantitative assessment of valve geometry and pathological properties in all heart phases, as well as the changes achieved through surgery, we aim to provide a new 4D segmentation method. Methods: We propose a tracking-based approach combining gradient vector flow (GVF) and position-based dynamics (PBD). An open-state surface model of the valve is propagated through time to the closed state, attracted by the GVF field of the leaflet area. The PBD method ensures topological consistency during deformation. For evaluation, one expert in cardiac surgery annotated the closed-state leaflets in 10 TEE sequences of patients with normal and abnormal mitral valves, and defined the corresponding open-state models. Results: The average point-to-surface distance between the manual annotations and the final tracked model was 1.00mm±1.08mm. Qualitatively, four cases were satisfactory, five passable and one unsatisfactory. Each sequence could be segmented in 2–6 min. Conclusion: Our approach enables to segment the mitral valve in 4D TEE image data with normal and pathological valve closing behavior. With this method, in addition to the quantification of the remaining orifice area, shape and dimensions of the coaptation zone can be analyzed and considered for planning and surgical result assessment.},
author = {Tautz, Lennart and Walczak, Lars and Georgii, Joachim and Jazaerli, Amer and Vellguth, Katharina and Wamala, Isaac and S{\"{u}}ndermann, Simon and Falk, Volkmar and Hennemuth, Anja},
doi = {10.1007/s11548-019-02071-4},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {Echocardiography,Mitral valve,Position-based dynamics,Segmentation,Tracking},
number = {1},
pages = {119--128},
title = {{Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074526687{\&}doi=10.1007{\%}2Fs11548-019-02071-4{\&}partnerID=40{\&}md5=d65f50a05a3b5b16c3d481c565ea1497},
volume = {15},
year = {2020}
}
@article{RN953,
abstract = {We introduce a region template abstraction and framework for the efficient storage, management and processing of common data types in analysis of large datasets of high resolution images on clusters of hybrid computing nodes. The region template abstraction provides a generic container template for common data structures, such as points, arrays, regions, and object sets, within a spatial and temporal bounding box. It allows for different data management strategies and I/O implementations, while providing a homogeneous, unified interface to applications for data storage and retrieval. A region template application is represented as a hierarchical dataflow in which each computing stage may be represented as another dataflow of finer-grain tasks. The execution of the application is coordinated by a runtime system that implements optimizations for hybrid machines, including performance-aware scheduling for maximizing the utilization of computing devices and techniques to reduce the impact of data transfers between CPUs and GPUs. An experimental evaluation on a state-of-the-art hybrid cluster using a microscopy imaging application shows that the abstraction adds negligible overhead (about 3{\%}) and achieves good scalability and high data transfer rates. Optimizations in a high speed disk based storage implementation of the abstraction to support asynchronous data transfers and computation result in an application performance gain of about 1.13x. Finally, a processing rate of 11,730 4K x 4K tiles per minute was achieved for the microscopy imaging application on a cluster with 100 nodes (300 GPUs and 1200 CPU cores). This computation rate enables studies with very large datasets.},
archivePrefix = {arXiv},
arxivId = {1405.7958},
author = {Teodoro, George and Pan, Tony and Kurc, Tahsin and Kong, Jun and Cooper, Lee and Klasky, Scott and Saltz, Joel},
doi = {10.1016/j.parco.2014.09.003},
eprint = {1405.7958},
issn = {01678191},
journal = {Parallel Computing},
keywords = {GPGPU,Heterogeneous environments,Image analysis,Microscopy imaging,Storage and I/O},
number = {10},
pages = {589--610},
title = {{Region templates: Data representation and management for high-throughput image analysis}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908545293{\&}doi=10.1016{\%}2Fj.parco.2014.09.003{\&}partnerID=40{\&}md5=811558beee3d6bf9758c3001bf55bb7b},
volume = {40},
year = {2014}
}
@article{RN799,
abstract = {A comprehensive methodology for treatment simulation and evaluation of dose coverage probabilities is presented where a population based statistical shape model (SSM) provide samples of fraction specific patient geometry deformations. The learning data consists of vector fields from deformable image registration of repeated imaging giving intra-patient deformations which are mapped to an average patient serving as a common frame of reference. The SSM is created by extracting the most dominating eigenmodes through principal component analysis of the deformations from all patients. The sampling of a deformation is thus reduced to sampling weights for enough of the most dominating eigenmodes that describe the deformations. For the cervical cancer patient datasets in this work, we found seven eigenmodes to be sufficient to capture 90{\%} of the variance in the deformations of the, and only three eigenmodes for stability in the simulated dose coverage probabilities. The normality assumption of the eigenmode weights was tested and found relevant for the 20 most dominating eigenmodes except for the first. Individualization of the SSM is demonstrated to be improved using two deformation samples from a new patient. The probabilistic evaluation provided additional information about the trade-offs compared to the conventional single dataset treatment planning.},
author = {Tilly, David and {Van De Schoot}, Agustinus J.A.J. and Grusell, Erik and Bel, Arjan and Ahnesj{\"{o}}, Anders},
doi = {10.1088/1361-6560/aa64ef},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {cervix,deformable image registration,probabilistic planning,radiotherapy,statistical shape model},
number = {10},
pages = {4140--4159},
title = {{Dose coverage calculation using a statistical shape model - Applied to cervical cancer radiotherapy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {62},
year = {2017}
}
@inproceedings{RN952,
abstract = {Concentric tube robots may enable new, safer minimally invasive surgical procedures by moving along curved paths to reach difficult-to-reach sites in a patient's anatomy. Operating these devices is challenging due to their complex, unintuitive kinematics and the need to avoid sensitive structures in the anatomy. In this paper, we present a motion planning method that computes collision-free motion plans for concentric tube robots at interactive rates. Our method's high speed enables a user to continuously and freely move the robot's tip while the motion planner ensures that the robot's shaft does not collide with any anatomical obstacles. Our approach uses a highly accurate mechanical model of tube interactions, which is important since small movements of the tip position may require large changes in the shape of the device's shaft. Our motion planner achieves its high speed and accuracy by combining offline precomputation of a collision-free roadmap with online position control. We demonstrate our interactive planner in a simulated neurosurgical scenario where a user guides the robot's tip through the environment while the robot automatically avoids collisions with the anatomical obstacles.},
author = {Torres, Luis G. and Baykal, Cenk and Alterovitz, Ron},
booktitle = {Proceedings - IEEE International Conference on Robotics and Automation},
doi = {10.1109/ICRA.2014.6907112},
isbn = {10504729 (ISSN)},
issn = {10504729},
pages = {1915--1921},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Interactive-rate motion planning for concentric tube robots}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929208407{\&}doi=10.1109{\%}2FICRA.2014.6907112{\&}partnerID=40{\&}md5=fda323933ad36f2a50b9d3d5493cdb34},
year = {2014}
}
@article{RN813,
abstract = {Quantitative imaging features (radiomics) extracted from apparent diffusion coefficient (ADC) maps of rectal cancer patients can provide additional information to support treatment decision. Most available radiomic computational packages allow extraction of hundreds to thousands of features. However, two major factors can influence the reproducibility of radiomic features: interobserver variability, and imaging filtering applied prior to features extraction. In this exploratory study we seek to determine to what extent various commonly-used features are reproducible with regards to the mentioned factors using ADC maps from two different clinics (56 patients). Features derived from intensity distribution histograms are less sensitive to manual tumour delineation differences, noise in ADC images, pixel size resampling and intensity discretization. Shape features appear to be strongly affected by delineation quality. On the whole, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing perturbations we reproduced.},
author = {Traverso, Alberto and Kazmierski, Michal and Shi, Zhenwei and Kalendralis, Petros and Welch, Mattea and Nissen, Henrik Dahl and Jaffray, David and Dekker, Andre and Wee, Leonard},
doi = {10.1016/j.ejmp.2019.04.009},
issn = {1724191X},
journal = {Physica Medica},
keywords = {Apparent diffusion coefficient,Diffusion weighted imaging,Locally advanced rectal carcinoma,Magnetic resonance imaging,Radiomic feature reproducibility},
pages = {44--51},
title = {{Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {61},
year = {2019}
}
@inproceedings{RN906,
abstract = {{\textcopyright} 2017 SPIE. Image registration of preprocedural contrast-enhanced CTs to intraprocedual cone-beam computed tomography (CBCT) can provide additional information for interventional liver oncology procedures such as transcatheter arterial chemoembolisation (TACE). In this paper, a novel similarity metric for gradient-based image registration is proposed. The metric relies on the patch-based computation of histograms of oriented gradients (HOG) building the basis for a feature descriptor. The metric was implemented in a framework for rigid 3D-3D-registration of pre-interventional CT with intra-interventional CBCT data obtained during the workflow of a TACE. To evaluate the performance of the new metric, the capture range was estimated based on the calculation of the mean target registration error and compared to the results obtained with a normalized cross correlation metric. The results show that 3D HOG feature descriptors are suitable as image-similarity metric and that the novel metric can compete with established methods in terms of registration accuracy.},
author = {Trimborn, Barbara and Wolf, Ivo and Abu-Sammour, Denis and Henzler, Thomas and Schad, Lothar R. and Z{\"{o}}llner, Frank G.},
booktitle = {Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling},
doi = {10.1117/12.2255601},
editor = {Webster, R J and Fei, B},
isbn = {9781510607156},
issn = {16057422},
pages = {101350C},
publisher = {SPIE},
title = {{Investigation of 3D histograms of oriented gradients for image-based registration of CT with interventional CBCT}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020450535{\&}doi=10.1117{\%}2F12.2255601{\&}partnerID=40{\&}md5=d433d81da054c950f6c4f2534eda48af},
volume = {10135},
year = {2017}
}
@article{RN803,
abstract = {We report here a multipurpose dynamic-interface-based segmentation tool, suitable for segmenting planar, cylindrical, and spherical surfaces in 3D. The method is fast enough to be used conveniently even for large images. Its implementation is straightforward and can be easily realized in many environments. Its memory consumption is low, and the set of parameters is small and easy to understand. The method is based on the Edwards-Wilkinson equation, which is traditionally used to model the equilibrium fluctuations of a propagating interface under the influence of temporally and spatially varying noise. We report here an adaptation of this equation into multidimensional image segmentation, and its efficient discretization.},
author = {Turpeinen, Tuomas and Myllys, Markko and Kekalainen, Pekka and Timonen, Jussi},
doi = {10.1109/TIP.2015.2484061},
issn = {10577149},
journal = {IEEE Transactions on Image Processing},
keywords = {Gray-scale,Image segmentation,Mathematical model,Noise,Surface morphology,Surface topography,Three-dimensional displays},
number = {12},
pages = {5696--5705},
title = {{Interface Detection Using a Quenched-Noise Version of the Edwards-Wilkinson Equation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {24},
year = {2015}
}
@inproceedings{RN835,
abstract = {Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is one of the vital steps for quantitative analysis of brain for further inspection. In this paper, NeuroNet has been adopted to segment the brain tissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF)) which uses Residual Network (ResNet) in encoder and Fully Convolution Network (FCN) in the decoder. To achieve the best performance, various hyper-parameters have been tuned, while, network parameters (kernel and bias) were initialized using the NeuroNet pre-trained model. Different pre-processing pipelines have also been introduced to get a robust trained model. The model has been trained and tested on IBSR18 data-set. To validate the research outcome, performance was measured quantitatively using Dice Similarity Coefficient (DSC) and is reported on average as 0.84 for CSF, 0.94 for GM, and 0.94 for WM. The outcome of the research indicates that for the IBSR18 data-set, pre-processing and proper tuning of hyper-parameters for NeuroNet model have improvement in DSC for the brain tissue segmentation.},
author = {Tushar, Fakrul Islam and Alyafi, Basel and Hasan, Md Kamrul and Dahal, Lavsen},
booktitle = {2019 Joint 8th International Conference on Informatics, Electronics and Vision, ICIEV 2019 and 3rd International Conference on Imaging, Vision and Pattern Recognition, icIVPR 2019 with International Conference on Activity and Behavior Computing, ABC 2019},
doi = {10.1109/ICIEV.2019.8858515},
isbn = {9781728107868},
keywords = {Brain tissue segmentation,Dice Similarity Coefficient (DSC),Fully Convolution Network (FCN),IBSR18,Magnetic resonance imaging (MRI),NeuroNet,Residual Network (ResNet)},
pages = {223--227},
publisher = {Institute of Electrical and Electronics Engineers Inc.},
title = {{Brain tissue segmentation using neuronet with different pre-processing techniques}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85074060440{\&}doi=10.1109{\%}2FICIEV.2019.8858515{\&}partnerID=40{\&}md5=8ce695907c0589166ae123e5259efcf0},
year = {2019}
}
@inproceedings{RN925,
abstract = {Tumor contouring is a challenging task for physicians especially when functional as well as anatomical images are incorporated. This paper presents a semi-automated technique to solve this problem. The proposed method is a variant of the level-set segmentation where the initial region is based on the functional image and the speed function combines both images. The presented approach allows the user to balance between the information of two input images. The method was evaluated on registered head and neck PET-MRI image pairs using manually defined tumor contours as reference. The algorithm was tested for various types of tumors using different weights to combine the functional and the anatomical information. The best results showed good correlation with the reference (3{\%} volume difference and 80{\%} DICE similarity in average).},
author = {Urb{\'{a}}n, Szabolcs and Rusk{\'{o}}, L{\'{a}}szl{\'{o}} and Nagy, Antal},
booktitle = {Computational Vision and Medical Image Processing V - Proceedings of 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VipIMAGE 2015},
doi = {10.1201/b19241-35},
editor = {Tavares, J M R S and Jorge, R M N},
isbn = {9781138029262},
pages = {209--214},
publisher = {CRC Press/Balkema},
title = {{Semi-automatic tumor contouring method using PET and MRI medical images}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959298536{\&}partnerID=40{\&}md5=54e2f8c3e13fa638156bc0e79854a686},
year = {2016}
}
@inproceedings{RN942,
abstract = {The introduction of 4D image acquisition techniques has made it possible to analyse anatomical motion in vivo. With 4D computed tomography (CT), it is now possible to study the motion of joints leading to a deeper understanding of the role of morphology on joint motion and a better assessment of pathologies. Although 4D CT shows a lot of opportunities, the workload to process these 4D acquisitions has increased dramatically. A major part of this process is segmentation, the delineation of the objects of interest within the image volume. This paper presents an algorithm to accelerate this step by registering the segmentation of one frame onto the others. This results in a fast segmentation of the whole 4D dataset, all identical in shape. We show that the proposed algorithm is able to segment two carpal bones, the trapezoid and the scaphoid, with results close to a manual segmentation in less than 5{\%} of the processing time.},
author = {{Van Dijck}, Christophe and Kerkhof, Faes and Vereecke, Evie and Wirix-Speetjens, Roel and {Vander Sloten}, Jos},
booktitle = {Proceedings - International Symposium on Biomedical Imaging},
doi = {10.1109/ISBI.2015.7163950},
isbn = {9781479923748},
issn = {19458452},
keywords = {4D CT,Registration,Segmentation},
pages = {621--624},
publisher = {IEEE Computer Society},
title = {{Segmentation of 4D CT bone images by sequential registration}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84944313955{\&}doi=10.1109{\%}2FISBI.2015.7163950{\&}partnerID=40{\&}md5=7b264d4c8cefb0383cb157bd19741c43},
volume = {2015-July},
year = {2015}
}
@article{RN895,
abstract = {Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www. radiomics.io. With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. Cancer Res; 77(21); e104-7.},
author = {{Van Griethuysen}, Joost J.M. and Fedorov, Andriy and Parmar, Chintan and Hosny, Ahmed and Aucoin, Nicole and Narayan, Vivek and Beets-Tan, Regina G.H. and Fillion-Robin, Jean Christophe and Pieper, Steve and Aerts, Hugo J.W.L.},
doi = {10.1158/0008-5472.CAN-17-0339},
issn = {15387445},
journal = {Cancer Research},
number = {21},
pages = {e104--e107},
pmid = {29092951},
title = {{Computational radiomics system to decode the radiographic phenotype}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85035021353{\&}doi=10.1158{\%}2F0008-5472.CAN-17-0339{\&}partnerID=40{\&}md5=e2bbcfe2a2e6a8ec00b02cf00ef7f2fa},
volume = {77},
year = {2017}
}
@inproceedings{RN927,
abstract = {In this paper we consider the problem of building parallel methods for three-dimensional human organs' geometrical reconstruction based on the active contour method. The idea of computing decomposition is based on opportunity to visit all layers in both directions from the starting layer and use several starting layers that have initial contours generated from the reference body geometrical model. Using ITK library we have implemented parallel kidney segmentation algorithms. The first parallel version gave acceleration of about 1.6 for 2 threads on CPU, and the second - a significant increase in efficiency with an increase in the number of points in the contour, but in the range 3.2 times for 8 threads on CPU. We are planning to transfer the algorithm on the GPU.},
author = {Vasil'ev, E. P. and Belokamenskaja, A. A. and Novozhilov, M. M. and Turlapov, V. E.},
booktitle = {CEUR Workshop Proceedings},
editor = {Starodubov, I and Sokolinsky, L},
isbn = {16130073 (ISSN)},
issn = {16130073},
keywords = {3D reconstruction,3D segmentation,Reference geometrical model,Segmentation,Tomography active contour method},
pages = {482--489},
publisher = {CEUR-WS},
title = {{A parallel algorithm for 3D reconstruction of internal organs according to imaging based on the active contour model}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978488492{\&}partnerID=40{\&}md5=ce7b9f7668691df1ee707e7ab33ab66e},
volume = {1576},
year = {2016}
}
@inproceedings{RN855,
abstract = {In this work, a semi-automatic computational strategy is proposed for brain tumor segmentation. The filtering (erosion + gaussian filters), segmentation (level set technique) and quantification (BT volume) stages are applied to magnetic resonance imaging in order to generate the three-dimensional morphology of brain tumors. The Jaccard's Similarity Index is considered to contrast manual segmentation with semi-automatic segmentations of brain tumor. In this sense, the highest Jaccard's Similarity Index provides the best parameters of the techniques that constitute the semi-automatic computational strategy. Results are promising, showing an excellent correlation between these segmentations. The volume is used for the brain tumors characterization.},
author = {Vera, M. and Hu{\'{e}}rfano, Y. and Gelvez, E. and Valbuena, O. and Salazar, J. and Molina, V. and Vera, M. I. and Salazar, W. and S{\'{a}}enz, F.},
booktitle = {Journal of Physics: Conference Series},
doi = {10.1088/1742-6596/1160/1/012002},
editor = {E.D, V -Nino and E.D, V -Nino and Almeida, E G},
isbn = {17426588 (ISSN)},
issn = {17426596},
number = {1},
publisher = {Institute of Physics Publishing},
title = {{Segmentation of brain tumors using a semi-automatic computational strategy}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062482878{\&}doi=10.1088{\%}2F1742-6596{\%}2F1160{\%}2F1{\%}2F012002{\&}partnerID=40{\&}md5=03a70ac3ff75e964350486d67a57b008},
volume = {1160},
year = {2019}
}
@article{RN940,
abstract = {To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low- from intermediate/high-grade PCas. The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS≥7. The dataset included 49 clinically significant PCas with a balanced distribution of pGS. The Spearman $\rho$ and AUC values on ADC were:-0.489, 0.823 (mean);-0.522, 0.821 (median);-0.569, 0.854 (10th percentile);-0.556, 0.854 (25th percentile);-0.386, 0.871 (C); 0.533, 0.923 (H); while on T2w they were:-0.654, 0.945 (C); 0.645, 0.962 (H). AUC of H on ADC and T2w, and C on T2w were significantly higher than that of the mean ADC (p = 0.05). H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2w MR imaging in assessing PCa biological aggressiveness.},
author = {Vignati, A. and Mazzetti, S. and Giannini, V. and Russo, F. and Bollito, E. and Porpiglia, F. and Stasi, M. and Regge, D.},
doi = {10.1088/0031-9155/60/7/2685},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {ADC maps,GLCM texture feature,T2-weighted MR imaging,pathologic Gleason score correlation,prostate cancer aggressiveness},
number = {7},
pages = {2685--2701},
title = {{Texture features on T2-weighted magnetic resonance imaging: New potential biomarkers for prostate cancer aggressiveness}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925609486{\&}doi=10.1088{\%}2F0031-9155{\%}2F60{\%}2F7{\%}2F2685{\&}partnerID=40{\&}md5=4b5ae2306deef2604c6595214b20549c},
volume = {60},
year = {2015}
}
@article{RN876,
abstract = {Recent improvements in cardiac computed tomography (CCT) allow for whole-heart functional studies to be acquired at low radiation dose ({\textless}2mSv) and high-temporal resolution ({\textless}100ms) in a single heart beat. Although the extraction of regional functional information from these images is of great clinical interest, there is a paucity of research into the quantification of regional function from CCT, contrasting with the large body of work in echocardiography and cardiac MR. Here we present the Simultaneous Subdivision Surface Registration (SiSSR) method: a fast, semi-automated image analysis pipeline for quantifying regional function from contrast-enhanced CCT. For each of thirteen adult male canines, we construct an anatomical reference mesh representing the left ventricular (LV) endocardium, obviating the need for a template mesh to be manually sculpted and initialized. We treat this generated mesh as a Loop subdivision surface, and adapt a technique previously described in the context of 3-D echocardiography to register these surfaces to the endocardium efficiently across all cardiac frames simultaneously. Although previous work performs the registration at a single resolution, we observe that subdivision surfaces naturally suggest a multiresolution approach, leading to faster convergence and avoiding local minima. We additionally make two notable changes to the cost function of the optimization, explicitly encouraging plausible biological motion and high mesh quality. Finally, we calculate an accepted functional metric for CCT from the registered surfaces, and compare our results to an alternate state-of-the-art CCT method.},
author = {Vigneault, Davis M. and Pourmorteza, Amir and Thomas, Marvin L. and Bluemke, David A. and Noble, J. Alison},
doi = {10.1016/j.media.2018.03.009},
issn = {13618423},
journal = {Medical Image Analysis},
keywords = {Cardiac computed tomography,Loop subdivision surface,Personalized cardiac mesh generation,Regional cardiac function},
pages = {215--228},
title = {{SiSSR: Simultaneous subdivision surface registration for the quantification of cardiac function from computed tomography in canines}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044919491{\&}doi=10.1016{\%}2Fj.media.2018.03.009{\&}partnerID=40{\&}md5=24f381dd27d1b120828f01ad803dafe5},
volume = {46},
year = {2018}
}
@article{RN920,
abstract = {Purpose: Magnetic particle imaging (MPI) is a quantitative imaging modality that allows the distribution of superparamagnetic nanoparticles to be visualized. Compared to other imaging techniques like x-ray radiography, computed tomography (CT), and magnetic resonance imaging (MRI), MPI only provides a signal from the administered tracer, but no additional morphological information, which complicates geometry planning and the interpretation of MP images. The purpose of the authors study was to develop bimodal fiducial markers that can be visualized by MPI and MRI in order to create MP-MR fusion images. Methods: A certain arrangement of three bimodal fiducial markers was developed and used in a combined MRI/MPI phantom and also during in vivo experiments in order to investigate its suitability for geometry planning and image fusion. An algorithm for automated marker extraction in both MR and MP images and rigid registration was established. Results: The developed bimodal fiducial markers can be visualized by MRI and MPI and allow for geometry planning as well as automated registration and fusion of MRMP images. Conclusions: To date, exact positioning of the object to be imaged within the field of view (FOV) and the assignment of reconstructed MPI signals to corresponding morphological regions has been difficult. The developed bimodal fiducial markers and the automated image registration algorithm help to overcome these difficulties.},
author = {Werner, F. and Jung, C. and Hofmann, M. and Werner, R. and Salamon, J. and S{\"{a}}ring, D. and Kaul, M. G. and Them, K. and Weber, O. M. and Mummert, T. and Adam, G. and Ittrich, H. and Knopp, T.},
doi = {10.1118/1.4948998},
issn = {00942405},
journal = {Medical Physics},
keywords = {MPI,MRI,fiducialmarker,geometry planning,registration},
number = {6},
pages = {2884--2893},
title = {{Geometry planning and image registration in magnetic particle imaging using bimodal fiducial markers}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84969765405{\&}doi=10.1118{\%}2F1.4948998{\&}partnerID=40{\&}md5=6ef63d0a878e48dd0de13440e94b2c70},
volume = {43},
year = {2016}
}
@article{RN949,
abstract = {The lung parenchyma provides a maximal surface area of blood-containing capillaries that are in close contact with a large surface areaof the air-containing alveoli. Volume and surface area of capillaries are the classic stereological parameters to characterize the alveolar capillary network (ACN) and have provided essential structure-function information of the lung. When loss (rarefaction) or gain (angiogenesis) of capillaries occurs, these parameters may not be sufficient to provide mechanistic insight. Therefore, it would be desirable to estimate the number of capillaries, as it contains more distinct and mechanistically oriented information. Here, we present a new stereological method to estimate the number of capillary loops in the ACN. One advantage of this method is that it is independent of the shape, size, or distribution of the capillaries. We used consecutive, 1 {\_}mthick sections from epoxy resin-embedded material as a physical disector. The Euler-Poincar{\'{e}} characteristic of capillary networks can be estimated by counting the easily recognizable topolog ical constellations of “islands,” “bridges,” and “holes.” The total number of capillary loops in the ACN can then be calculated from the Euler- Poincar{\'{e}} characteristic. With the use of the established estimator of alveolar number, it is possible to obtain the mean number of capillary loops per alveolus. In conclusion, estimation of alveolar capillaries by design-based stereology is an efficient and unbiased method to characterize the ACN and may be particularly useful for studies on emphysema, pulmonary hypertension, or lung development.},
author = {Willf{\"{u}}hr, Alper and Brandenberger, Christina and Piatkowski, Tanja and Grothausmann, Roman and Nyengaard, Jens Randel and Ochs, Matthias and M{\"{u}}hlfeld, Christian},
doi = {10.1152/ajplung.00410.2014},
issn = {15221504},
journal = {American Journal of Physiology - Lung Cellular and Molecular Physiology},
keywords = {Capillary number,Euler number,Stereology},
number = {11},
pages = {L1286--L1293},
title = {{Estimation of the number of alveolar capillaries by the euler number (Euler-poincar{\'{e}} characteristic)}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949526543{\&}doi=10.1152{\%}2Fajplung.00410.2014{\&}partnerID=40{\&}md5=56c6058ebfd732edb1588ab2e55b6ec9},
volume = {309},
year = {2015}
}
@inproceedings{RN870,
abstract = {A rigid registration is a crucial initial step for a correct deformable medical image registration. In this work, we propose rigid registration method resistant to large deformations and missing data. The proposed method is based on the bones segmentation, feature matching and outliers elimination inspired by traditional computer vision approach. The method is compared to other state-of-the-art algorithms, the iterative closest point and intensity-based registration using widely available dataset. The proposed algorithm does not fail into local minima and reconstructs correct deformations for average vector length greater than 150 mm and data overlap ratio less than 50{\%}, where currently applied methods fail. The algorithm is evaluated using angle and magnitude errors between corresponding deformation vectors, Hausdorff distance between bone segmentations and resistance to fail into local minima.},
author = {Wodzinski, Marek and Skalski, Andrzej},
booktitle = {International Conference on Systems, Signals, and Image Processing},
doi = {10.1109/IWSSIP.2018.8439679},
editor = {Zamuda, A and Planinsic, P and Gleich, D},
isbn = {9781538669792},
issn = {21578702},
keywords = {Image Registration,Initial Alignment,Medical Imaging,Rigid Registration},
publisher = {IEEE Computer Society},
title = {{Rigid Registration Method for Medical Volumes with Large Deformations and Missing Data}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053151071{\&}doi=10.1109{\%}2FIWSSIP.2018.8439679{\&}partnerID=40{\&}md5=c8fc60e15ec9db845e0390f82b94df27},
volume = {2018-June},
year = {2018}
}
@article{RN913,
abstract = {Prefailure microdamage in bone tissue is considered to be the most detrimental factor in defining its strength and toughness with respect to age and disease. To understand the influence of microcracks on bone mechanics it is necessary to assess their morphology and three-dimensional distribution. This requirement reaches beyond classic histology and stereology, and methods to obtain such information are currently missing. Therefore, the aim of the study was to develop a methodology that allows to characterize three-dimensional microcrack distributions in bulk bone samples. Four dumbbell-shaped specimens of human cortical bone of a 77-year-old female donor were loaded beyond yield in either tension, compression or torsion (one control). Subsequently, synchrotron radiation micro-computed tomography (SR$\mu$CT) was used to obtain phase-contrast images of the damaged samples. A microcrack segmentation algorithm was developed and used to segment microcrack families for which microcrack orientation distribution functions were determined. Distinct microcrack families were observed for each load case that resulted in distinct orientation distribution functions. Microcracks had median areas of approximately 4.7 $\mu$m2, 33.3 $\mu$m2 and 64.0 $\mu$m2 for tension, compression and torsion. Verifying the segmentation algorithm against a manually segmented ground truth showed good results when comparing the microcrack orientation distribution functions. A size dependence was noted when investigating the orientation distribution functions with respect to the size of the volume of interest used for their determination. Furthermore, a scale separation between tensile, compressive and torsional microcracks was noticeable. Visual comparison to classic histology indicated that microcrack families were successfully distinguished. We propose a methodology to analyse three-dimensional microcrack distributions in overloaded cortical bone. Such information could improve our understanding of bone microdamage and its impact on bone failure in relation to tissue age and disease.},
author = {Wolfram, U. and Schwiedrzik, J. J. and Mirzaali, M. J. and B{\"{U}}RKI, A. and Varga, P. and Olivier, C. and Peyrin, F. and Zysset, P. K.},
doi = {10.1111/jmi.12440},
issn = {13652818},
journal = {Journal of Microscopy},
keywords = {Cortical bone,X-ray phase micro-tomography,microcrack segmentation,microdamage,orientation distribution function,synchrotron radiation},
number = {3},
pages = {268--281},
title = {{Characterizing microcrack orientation distribution functions in osteonal bone samples}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978744015{\&}doi=10.1111{\%}2Fjmi.12440{\&}partnerID=40{\&}md5=eaacef5f6889af31fbaf82da8a2c4344},
volume = {264},
year = {2016}
}
@article{RN796,
abstract = {In large scale biological experiments, like high-throughput or high-content cellular screening, the amount and the complexity of images to be analyzed are steadily increasing. To handle and process these images, well defined image processing and analysis steps need to be performed by applying dedicated workflows. Multiple software tools have emerged with the aim to facilitate creation of such workflows by integrating existing methods, tools, and routines, and by adapting them to different applications and questions, as well as making them reusable and interchangeable. In this review, we describe workflow systems for the integration of microscopy image analysis techniques with focus on KNIME and Galaxy.},
author = {Wollmann, Thomas and Erfle, Holger and Eils, Roland and Rohr, Karl and Gunkel, Manuel},
doi = {10.1016/j.jbiotec.2017.07.019},
issn = {18734863},
journal = {Journal of Biotechnology},
keywords = {Galaxy,Image analysis,KNIME,Microscopy,Pipeline,Workflow,de.NBI},
pages = {70--75},
title = {{Workflows for microscopy image analysis and cellular phenotyping}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {261},
year = {2017}
}
@inproceedings{RN976,
abstract = {Selecting regions of interest (ROI) of the medical images is an important task in medical image processing. Manual selection of ROIs serves as the main method for single images and it has a high accuracy. However, it will become infeasible to manually segment ROIs on a large number of images. Observing this problem, this paper proposes a fast and accurate segmentation method to obtain ROIs on a batch of medical images. Firstly, we segment the standard brain image St which has not been injected with tracer. Secondly, we use a B-Spline elastic registration method to get the inverse-registration parameters. Thirdly, we get the template image Te with the registration parameters. Finally, we search the target region by template matching. Experimental results show that the proposed method performs well on medical image segmentation.},
author = {Wu, Jiatao and Li, Yong and Peng, Yun and Fan, Chunxiao},
booktitle = {IS and T International Symposium on Electronic Imaging Science and Technology},
doi = {10.2352/ISSN.2470-1173.2017.2.VIPC-404},
editor = {Stevenson, R L and Delp, E},
isbn = {24701173 (ISSN)},
issn = {24701173},
pages = {38--43},
publisher = {Society for Imaging Science and Technology},
title = {{A fast and accurate segmentation method for medical images}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85041527605{\&}doi=10.2352{\%}2FISSN.2470-1173.2017.2.VIPC-404{\&}partnerID=40{\&}md5=611236c5db623b23e4984bfc71e20bb2},
year = {2017}
}
@article{RN784,
abstract = {Typical packages used for coregistration in functional image analyses include automated image registration (AIR) and statistical parametric mapping (SPM). However, both methods have limited-dimension deformation models. A fully deformable model, which combines the piecewise linear registration for coarse alignment with demons algorithm for voxel-level refinement, allows a higher degree of spatial deformation. This leads to a more accurate colocalization of the functional signal from different subjects and therefore can produce a more reliable group average signal. We quantitatively compared the performance of the three different registration approaches through a series of experiments and we found that the fully deformable model consistently produces a more accurate structural segmentation and a more reliable functional signal colocalization than does AIR or SPM. {\textcopyright} 2006 Wiley-Liss, Inc.},
author = {Wu, Minjie and Carmichael, Owen and Lopez-Garcia, Pilar and Carter, Cameron S. and Aizenstein, Howard J.},
doi = {10.1002/hbm.20216},
issn = {10659471},
journal = {Human Brain Mapping},
keywords = {Atlas-based segmentation,Deformable model,Image registration,fMRI},
number = {9},
pages = {747--754},
title = {{Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images}},
type = {Journal Article},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.20216},
volume = {27},
year = {2006}
}
@article{RN909,
abstract = {Ultrasound tomography (UST) image segmentation is fundamental in breast density estimation, medicine response analysis, and anatomical change quantification. Existing methods are time consuming and require massive manual interaction. To address these issues, an automatic algorithm based on GrabCut (AUGC) is proposed in this paper. The presented method designs automated GrabCut initialization for incomplete labeling and is sped up with multicore parallel programming. To verify performance, AUGC is applied to segment thirty-two in vivo UST volumetric images. The performance of AUGC is validated with breast overlapping metrics (Dice coefficient (D), Jaccard (J), and False positive (FP)) and time cost (TC). Furthermore, AUGC is compared to other methods, including Confidence Connected Region Growing (CCRG), watershed, and Active Contour based Curve Delineation (ACCD). Experimental results indicate that AUGC achieves the highest accuracy (D=0.9275 and J=0.8660 and FP=0.0077) and takes on average about 4 seconds to process a volumetric image. It was said that AUGC benefits large-scale studies by using UST images for breast cancer screening and pathological quantification.},
author = {Wu, Shibin and Yu, Shaode and Zhuang, Ling and Wei, Xinhua and Sak, Mark and Duric, Neb and Hu, Jiani and Xie, Yaoqin},
doi = {10.1155/2017/2059036},
issn = {23146141},
journal = {BioMed Research International},
title = {{Automatic Segmentation of Ultrasound Tomography Image}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030642114{\&}doi=10.1155{\%}2F2017{\%}2F2059036{\&}partnerID=40{\&}md5=c0598599370d8f954d410519826406e2},
volume = {2017},
year = {2017}
}
@article{RN849,
abstract = {Purpose: Diagnostic imaging procedures require optimization depending on the medical task at hand, the apparatus being used, and patient physical and anatomical characteristics. The assessment of the radiation dose and associated risks plays a key role in safety and quality management for radiation protection purposes. In this work, we aim at developing a methodology for personalized organ-level dose assessment in x-ray computed tomography (CT) imaging. Methods: Regional voxel models representing reference patient-specific computational phantoms were generated through image segmentation of CT images for four patients. The best-fitting anthropomorphic phantoms were selected from a previously developed comprehensive phantom library according to patient's anthropometric parameters, then registered to the anatomical masks (skeleton, lung, and body contour) of patients to produce a patient-specific whole-body phantom. Well-established image registration metrics including Jaccard's coefficients for each organ, organ mass, body perimeter, organ-surface distance, and effective diameter are compared between the reference patient model, registered model, and anchor phantoms. A previously validated Monte Carlo code is utilized to calculate the absorbed dose in target organs along with the effective dose delivered to patients. The calculated absorbed doses from the reference patient models are then compared with the produced personalized model, anchor phantom, and those reported by commercial dose monitoring systems. Results: The evaluated organ-surface distance and body effective diameter metrics show a mean absolute difference between patient regional voxel models, serving as reference, and patient-specific models around 4.4{\%} and 4.5{\%}, respectively. Organ-level radiation doses of patient-specific models are in good agreement with those of the corresponding patient regional voxel models with a mean absolute difference of 9.1{\%}. The mean absolute difference of organ doses for the best-fitting model extracted from the phantom library and Radimetrics™ commercial dose tracking software are 15.5{\%} and 41.1{\%}, respectively. Conclusion: The results suggest that the proposed methodology improves the accuracy of organ-level dose estimation in CT, especially for extreme cases [high body mass index (BMI) and large skeleton]. Patient-specific radiation dose calculation and risk assessment can be performed using the proposed methodology for both monitoring of cumulative radiation exposure of patients and epidemiological studies. Further validation using a larger database is warranted.},
author = {Xie, Tianwu and Akhavanallaf, Azadeh and Zaidi, Habib},
doi = {10.1002/mp.13471},
issn = {00942405},
journal = {Medical Physics},
keywords = {Monte Carlo simulations,computational models,radiation dose,radiological imaging},
number = {5},
pages = {2403--2411},
title = {{Construction of patient-specific computational models for organ dose estimation in radiological imaging}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063261918{\&}doi=10.1002{\%}2Fmp.13471{\&}partnerID=40{\&}md5=7e6f21940c6d8f7bbd4b733b927a0667},
volume = {46},
year = {2019}
}
@article{RN867,
abstract = {The radiation dose delivered to pregnant patients during radiologic imaging procedures raises health concerns because the developing embryo and fetus are considered to be highly radiosensitive. To appropriately weigh the diagnostic benefits against the radiation risks, the radiologist needs reasonably accurate and detailed estimates of the fetal dose. Expanding our previously developed series of computational phantoms for pregnant women, we here describe a personalized model for twin pregnancy, based on an actual clinical scan. Methods: The model is based on a standardized hybrid pregnant female and fetus phantom and on a clinical case of a patient who underwent an 18F-FDG PET/CT scan while expecting twins at 25 weeks' gestation. This model enabled us to produce a realistic physical representation of the pregnant patient and to estimate the maternal and fetal organ doses from the 18FFDG and CT components. The Monte Carlo N-Particle Extended general-purpose code was used for radiation transport simulation. Results: The 18F-FDG doses for the 2 fetuses were 3.78 and 3.99 mGy, and the CT doses were 0.76 and 0.70 mGy, respectively. Therefore, the relative contribution of 18F-FDG and CT to the total dose to the fetuses was about 84{\%} and 16{\%}, respectively. Meanwhile, for 18F-FDG, the calculated personalized absorbed dose was about 40{\%}-50{\%} higher than the doses reported by other dosimetry computer software tools. Conclusion: Our approach to constructing personalized computational models allows estimation of a patient- specific radiation dose, even in cases with unusual anatomic features such as a twin pregnancy. Our results also show that, even in twins, the fetal organ doses from both 18F-FDG and CT present a certain variability linked to the anatomic characteristics. The CT fetal dose is smaller than the 18F-FDG PET dose.},
author = {Xie, Tianwu and Zanotti-Fregonara, Paolo and Edet-Sanson, Agathe and Zaidi, Habib},
doi = {10.2967/jnumed.117.205286},
issn = {2159662X},
journal = {Journal of Nuclear Medicine},
keywords = {CT,Fetus,Monte Carlo simulation,Pregnant female models,Radiation dosimetry},
number = {9},
pages = {1451--1458},
title = {{Patient-specific computational model and dosimetry calculations for PET/CT of a patient pregnant with twins}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053206243{\&}doi=10.2967{\%}2Fjnumed.117.205286{\&}partnerID=40{\&}md5=2ee14ea7a0f6a85f10442957bf4c6b2e},
volume = {59},
year = {2018}
}
@article{RN861,
abstract = {Heterogeneity of echo-texture and lack of sharply delineated tissue boundaries in diagnostic ultrasound images make three-dimensional (3D) registration challenging, especially when the volumes to be registered are considerably different due to local changes. We implemented a novel computational method that optimally registers volumetric ultrasound image data containing significant and local anatomical differences. It is A Multi-stage, Multi-resolution, and Multi-volumes-of-interest Volume Registration Method. A single region registration is optimized first for a close initial alignment to avoid convergence to a locally optimal solution. Multiple sub-volumes of interest can then be selected as target alignment regions to achieve confident consistency across the volume. Finally, a multi-resolution rigid registration is performed on these sub-volumes associated with different weights in the cost function. We applied the method on 3D endovaginal ultrasound image data acquired from patients during biopsy procedure of the pelvic floor muscle. Systematic assessment of our proposed method through cross validation demonstrated its accuracy and robustness. The algorithm can also be applied on medical imaging data of other modalities for which the traditional rigid registration methods would fail.},
author = {Xing, Qi and Chitnis, Parag and Sikdar, Siddhartha and Alshiek, Jonia and {Abbas Shobeiri}, S. and Wei, Qi},
doi = {10.1371/journal.pone.0224583},
issn = {19326203},
journal = {PLoS ONE},
number = {11},
title = {{M3VR—A multi-stage, multi-resolution, and multi-volumes-of-interest volume registration method applied to 3D endovaginal ultrasound}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85075267830{\&}doi=10.1371{\%}2Fjournal.pone.0224583{\&}partnerID=40{\&}md5=18cd44cc5e14de2956edb1e316ba170a},
volume = {14},
year = {2019}
}
@article{RN810,
abstract = {This paper had published originally without open access, but has since been republished with open access.},
author = {Yaniv, Ziv and Lowekamp, Bradley C. and Johnson, Hans J. and Beare, Richard},
doi = {10.1007/s10278-018-0165-9},
issn = {1618727X},
journal = {Journal of Digital Imaging},
number = {6},
pages = {1118},
title = {{Correction to: SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research (Journal of Digital Imaging, (2018), 31, 3, (290-303), 10.1007/s10278-017-0037-8)}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {32},
year = {2019}
}
@article{RN872,
abstract = {The stiffness of foam concrete depends primarily on the added porosity. Nevertheless, by performing 3D elastic numerical simulations on artificial unit cells in the frame of periodic homogenization, it is shown that describing foam concrete as a porous material is not sufficient to explain the experimental measurements of the Young modulus for added porosity higher than 40{\%}. Indeed, introducing sand as a third phase enables to recover accurate estimates of the Young modulus. Furthermore, for highly porous concrete foams, it is shown that the stress concentrates in thin members deprived of stiff sand particles, thus leading to a softer overall stiffness.},
author = {Youssef, M. Ben and Lavergne, F. and Sab, K. and Miled, K. and Neji, J.},
doi = {10.1016/j.cemconres.2018.04.021},
issn = {00088846},
journal = {Cement and Concrete Research},
keywords = {Foam concrete,Homogenization,Microstructure,Numerical simulations,Young modulus},
pages = {13--23},
title = {{Upscaling the elastic stiffness of foam concrete as a three-phase composite material}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046623861{\&}doi=10.1016{\%}2Fj.cemconres.2018.04.021{\&}partnerID=40{\&}md5=2e128f4fc86a6df7726434940ab8ceb7},
volume = {110},
year = {2018}
}
@article{RN820,
abstract = {Multi atlas based segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases. Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split into two groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle selection, where atlases are chosen by the highest dice similarity coefficient (DSC) with the target, was performed. This was compared to oracle group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, convolutional neural networks were designed to predict the best group of atlases. The results were also compared with the selection strategy based on normalized mutual information (NMI). Oracle group was proven to be significantly better than classic oracle selection (p {\textless} 10-5). Atlas group selection led to a median ± interquartile DSC of 0.740 ± 0.084, 0.718 ± 0.086 and 0.670 ± 0.097 for brainstem and left/right parotid glands respectively, outperforming NMI selection 0.676 ± 0.113, 0.632 ± 0.104 and 0.606 ± 0.118 (p {\textless} 0.001) as well as classic oracle selection. The implemented methodology is a proof of principle that selecting the atlases by considering the performance of the entire group of atlases instead of each single atlas leads to higher segmentation accuracy, being even better then current oracle strategy. This finding opens a new discussion about the most appropriate atlas selection criterion for MABS.},
author = {Zaffino, Paolo and Ciardo, Delia and Raudaschl, Patrik and Fritscher, Karl and Ricotti, Rosalinda and Alterio, Daniela and Marvaso, Giulia and Fodor, Cristiana and Baroni, Guido and Amato, Francesco and Orecchia, Roberto and Jereczek-Fossa, Barbara Alicja and Sharp, Gregory C. and Spadea, Maria Francesca},
doi = {10.1088/1361-6560/aac712},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {atlas selection,convolutional neural network,medical image segmentation,multi atlas based segmentation,oracle selection},
number = {12},
pages = {9},
title = {{Multi atlas based segmentation: Should we prefer the best atlas group over the group of best atlases?}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {63},
year = {2018}
}
@article{RN955,
abstract = {Background and objective: Modern microscopes can acquire multi-channel large histological data from tissues of human beings or animals, which contain rich biomedical information for disease diagnosis and biological feature analysis. However, due to the large size, fuzzy tissue structure, and complicated multiple elements integrated in the image color space, it is still a challenge for current software systems to effectively calculate histological data, show the inner tissue structures and unveil hidden biomedical information. Therefore, we developed new algorithms and a software platform to address this issue. Methods: This paper presents a multi-channel biomedical data computing and visualization system that can efficiently process large 3D histological images acquired from high-resolution microscopes. A novelty of our system is that it can dynamically display a volume of interest and extract tissue information using a layer-based data navigation scheme. During the data exploring process, the actual resolution of the loaded data can be dynamically determined and updated, and data rendering is synchronized in four display windows at each data layer, where 2D textures are extracted from the imaging volume and mapped onto the displayed clipping planes in 3D space. Results: To test the efficiency and scalability of this system, we performed extensive evaluations using several different hardware systems and large histological color datasets acquired from a CryoViz 3D digital system. The experimental results demonstrated that our system can deliver interactive data navigation speed and display detailed imaging information in real time, which is beyond the capability of commonly available biomedical data exploration software platforms. Conclusion: Taking advantage of both CPU (central processing unit) main memory and GPU (graphics processing unit) graphics memory, the presented software platform can efficiently compute, process and visualize very large biomedical data and enhance data information. The performance of this system can satisfactorily address the challenges of navigating and interrogating volumetric multi-spectral large histological image at multiple resolution levels.},
author = {Zhang, Qi and Peters, Terry and Fenster, Aaron},
doi = {10.1016/j.compmedimag.2019.01.004},
issn = {18790771},
journal = {Computerized Medical Imaging and Graphics},
keywords = {Biomedical information,Large histological data,Layer-based data navigation,Texture extraction and mapping,Visualization,Volume of interest},
pages = {34--46},
title = {{Layer-based visualization and biomedical information exploration of multi-channel large histological data}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061394368{\&}doi=10.1016{\%}2Fj.compmedimag.2019.01.004{\&}partnerID=40{\&}md5=da16d481a2565d81446681597a01b201},
volume = {72},
year = {2019}
}
@article{RN808,
abstract = {Purpose: The study is to explore potential features and develop classification models for distinguishing benign and malignant lung lesions based on CT-radiomics features and PET metabolic parameters extracted from PET/CT images. Materials and methods: A retrospective study was conducted in baseline 18 F-flurodeoxyglucose positron emission tomography/ computed tomography (18 F-FDG PET/CT) images of 135 patients. The dataset was utilized for feature extraction of CT-radiomics features and PET metabolic parameters based on volume of interest, then went through feature selection and model development with strategy of five-fold cross-validation. Specifically, model development used support vector machine, PET metabolic parameters selection used Akaike's information criterion, and CT-radiomics were reduced by the least absolute shrinkage and selection operator method then forward selection approach. The diagnostic performances of CT-radiomics, PET metabolic parameters and combination of both were illustrated by receiver operating characteristic (ROC) curves, and compared by Delong test. Five groups of selected PET metabolic parameters and CT-radiomics were counted, and potential features were found and analyzed with Mann-Whitney U test. Results: The CT-radiomics, PET metabolic parameters, and combination of both among five subsets showed mean area under the curve (AUC) of 0.820 ± 0.053, 0.874 ± 0.081, and 0.887 ± 0.046, respectively. No significant differences in ROC among models were observed through pairwise comparison in each fold (P-value from 0.09 to 0.81, Delong test). The potential features were found to be SurfaceVolumeRatio and SUVpeak (P {\textless} 0.001 of both, U test). Conclusion: The classification models developed by CT-radiomics features and PET metabolic parameters based on PET/CT images have substantial diagnostic capacity on lung lesions.},
author = {Zhang, Ruiping and Zhu, Lei and Cai, Zhengting and Jiang, Wei and Li, Jian and Yang, Chengwen and Yu, Chunxu and Jiang, Bo and Wang, Wei and Xu, Wengui and Chai, Xiangfei and Zhang, Xiaodong and Tang, Yong},
doi = {10.1016/j.ejrad.2019.108735},
issn = {18727727},
journal = {European Journal of Radiology},
keywords = {CT-radiomics features,Lung lesion,PET metabolic parameters,Potential feature},
pages = {9},
title = {{Potential feature exploration and model development based on 18F-FDG PET/CT images for differentiating benign and malignant lung lesions}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {121},
year = {2019}
}
@article{RN844,
abstract = {Linear registration is often the crucial first step for various types of image analysis. Although this is mathematically simple, failure is not uncommon. When investigating the brain by magnetic resonance imaging (MRI), the brain is the target organ for registration but the existence of other tissues, in addition to a variety of fields of view, different brain locations, orientations and anatomical features, poses some serious fundamental challenges. Consequently, a number of different algorithms have been

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