text:
texture features for the prediction of response to therapy in colorectal cancer: a preliminary study}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033363764{\&}doi=10.1007{\%}2Fs11547-017-0833-8{\&}partnerID=40{\&}md5=507a4f33003fadbdd5ac25f4d11e4428},
volume = {123},
year = {2018}
}
@article{Chabriais2005,
author = {Chabriais, J.},
doi = {10.1016/s0221-0363(05)81460-x},
issn = {02210363},
journal = {Journal de Radiologie},
number = {7-8},
pages = {864--867},
title = {{Informatique et imagerie m{\'{e}}dicale: Les tendances d{\'{e}}celables {\`{a}} Chicago en 2004}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {86},
year = {2005}
}
@article{Chen2019,
abstract = {PURPOSE: In prostate focal therapy, it is important to accurately localize malignant lesions in order to increase biological effect of the tumor region while achieving a reduction in dose to noncancerous tissue. In this work, we proposed a transfer learning-based deep learning approach, for classification of prostate lesions in multiparametric magnetic resonance imaging images. METHODS: Magnetic resonance imaging images were preprocessed to remove bias artifact and normalize the data. Two state-of-the-art deep convolutional neural network models, InceptionV3 and VGG-16, were pretrained on ImageNet data set and retuned on the multiparametric magnetic resonance imaging data set. As lesion appearances differ by the prostate zone that it resides in, separate models were trained. Ensembling was performed on each prostate zone to improve area under the curve. In addition, the predictions from lesions on each prostate zone were scaled separately to increase the area under the curve for all lesions combined. RESULTS: The models were tuned to produce the highest area under the curve on validation data set. When it was applied to the unseen test data set, the transferred InceptionV3 model achieved an area under the curve of 0.81 and the transferred VGG-16 model achieved an area under the curve of 0.83. This was the third best score among the 72 methods from 33 participating groups in ProstateX competition. CONCLUSION: The transfer learning approach is a promising method for prostate cancer detection on multiparametric magnetic resonance imaging images. Features learned from ImageNet data set can be useful for medical images.},
author = {Chen, Quan and Hu, Shiliang and Long, Peiran and Lu, Fang and Shi, Yujie and Li, Yunpeng},
doi = {10.1177/1533033819858363},
issn = {15330338},
journal = {Technology in cancer research {\&} treatment},
keywords = {AI,convolutional neural network,focal therapy,mpMRI,prostate lesion,transfer learning},
pages = {9},
pmid = {31221034},
title = {{A Transfer Learning Approach for Malignant Prostate Lesion Detection on Multiparametric MRI}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {18},
year = {2019}
}
@article{Chen2018,
abstract = {Multiparametric magnetic resonance imaging (mpMRI) has been established as the state-of-the-art examination for the detection and localization of prostate cancer lesions. Prostate Imaging-Reporting and Data System (PI-RADS) has been established as a scheme to standardize the reporting of mpMRI findings. Although lesion delineation and PI-RADS ratings could be performed manually, human delineation and ratings are subjective and time-consuming. In this article, we developed and validated a self-tuned graph-based model for PI-RADS rating prediction. 34 features were obtained at the pixel level from T2-weighted (T2W), apparent diffusion coefficient (ADC) and dynamic contrast enhanced (DCE) images, from which PI-RADS scores were predicted. Two major innovations were involved in this self-tuned graph-based model. First, graph-based approaches are sensitive to the choice of the edge weight. The proposed model tuned the edge weights automatically based on the structure of the data, thereby obviating empirical edge weight selection. Second, the feature weights were tuned automatically to give heavier weights to features important for PI-RADS rating estimation. The proposed framework was evaluated for its lesion localization performance in mpMRI datasets of 12 patients. In the evaluation, the PI-RADS score distribution map generated by the algorithm and from the observers' ratings were binarized by thresholds of 3 and 4. The sensitivity, specificity and accuracy obtained in these two threshold settings ranged from 65 to 77{\%}, 86 to 93{\%} and 85 to 88{\%} respectively, which are comparable to results obtained in previous studies in which non-clinical T2 maps were available. The proposed algorithm took 10s to estimate the PI-RADS score distribution in an axial image. The efficiency achievable suggests that this technique can be developed into a prostate MR analysis system suitable for clinical use after a thorough validation involving more patients.},
author = {Chen, Weifu and Lin, Mingquan and Gibson, Eli and Bastian-Jordan, Matthew and Cool, Derek W. and Kassam, Zahra and Liang, Huageng and Feng, Guocan and Ward, Aaron D. and Chiu, Bernard},
doi = {10.1016/j.compbiomed.2018.03.017},
issn = {18790534},
journal = {Computers in Biology and Medicine},
keywords = {Laplacian regularized regression model,Multiparametric MRI (mpMRI),Multiple kernel learning,Prostate Imaging and Reporting Data System (PI-RAD,Prostate cancer},
pages = {252--265},
title = {{A self-tuned graph-based framework for localization and grading prostate cancer lesions: An initial evaluation based on multiparametric magnetic resonance imaging}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {96},
year = {2018}
}
@article{Chi2011,
abstract = {A novel vessel context-based voting is proposed for automatic liver vasculature segmentation in CT images. It is able to conduct full vessel segmentation and recognition of multiple vasculatures effectively. The vessel context describes context information of a voxel related to vessel properties, such as intensity, saliency, direction, and connectivity. Voxels are grouped to liver vasculatures hierarchically based on vessel context. They are first grouped locally into vessel branches with the advantage of a vessel junction measurement and then grouped globally into vasculatures, which is implemented using a multiple feature point voting mechanism. The proposed method has been evaluated on ten clinical CT datasets. Segmentation of third-order vessel trees from CT images (0.76×0.76 2.0 mm) of the portal venous phase takes less than 3 min on a PC with 2.0 GHz dual core processor and the average segmentation accuracy is up to 98. {\textcopyright} 2011 IEEE.},
author = {Chi, Yanling and Liu, Jimin and Venkatesh, Sudhakar K. and Huang, Su and Zhou, Jiayin and Tian, Qi and Nowinski, Wieslaw L.},
doi = {10.1109/TBME.2010.2093523},
issn = {00189294},
journal = {IEEE Transactions on Biomedical Engineering},
keywords = {Liver vasculature segmentation,multiple feature point voting,vessel context,vessel junction measure},
number = {8},
pages = {2144--2153},
title = {{Segmentation of liver vasculature from contrast enhanced CT images using context-based voting}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {58},
year = {2011}
}
@article{Chincarini2011,
abstract = {Background: Medial temporal lobe (MTL) atrophy is one of the key biomarkers to detect early neurodegenerative changes in the course of Alzheimer's disease (AD). There is active research aimed at identifying automated methodologies able to extract accurate classification indexes from T1-weighted magnetic resonance images (MRI). Such indexes should be fit for identifying AD patients as early as possible. Subjects: A reference group composed of 144. AD patients and 189 age-matched controls was used to train and test the procedure. It was then applied on a study group composed of 302 MCI subjects, 136 having progressed to clinically probable AD (MCI-converters) and 166 having remained stable or recovered to normal condition after a 24. month follow-up (MCI-non converters). All subjects came from the ADNI database. Methods: We sampled the brain with 7 relatively small volumes, mainly centered on the MTL, and 2 control regions. These volumes were filtered to give intensity and textural MRI-based features. Each filtered region was analyzed with a Random Forest (RF) classifier to extract relevant features, which were subsequently processed with a Support Vector Machine (SVM) classifier. Once a prediction model was trained and tested on the reference group, it was used to compute a classification index (CI) on the MCI cohort and to assess its accuracy in predicting AD conversion in MCI patients. The performance of the classification based on the features extracted by the whole 9 volumes is compared with that derived from each single volume. All experiments were performed using a bootstrap sampling estimation, and classifier performance was cross-validated with a 20-fold paradigm. Results: We identified a restricted set of image features correlated with the conversion to AD. It is shown that most information originate from a small subset of the total available features, and that it is enough to give a reliable assessment. We found multiple, highly localized image-based features which alone are responsible for the overall clinical diagnosis and prognosis. The classification index is able to discriminate Controls from AD with an Area Under Curve (AUC) = 0.97 (sensitivity ≃ 89{\%} at specificity ≃ 94{\%}) and Controls from MCI-converters with an AUC = 0.92 (sensitivity ≃ 89{\%} at specificity ≃ 80{\%}). MCI-converters are separated from MCI-non converters with AUC = 0.74(sensitivity ≃ 72{\%} at specificity ≃ 65{\%}). Findings: The present automated MRI-based technique revealed a strong relationship between highly localized baseline-MRI features and the baseline clinical assessment. In addition, the classification index was also used to predict the probability of AD conversion within a time frame of two years. The definition of a single index combining local analysis of several regions can be useful to detect AD neurodegeneration in a typical MCI population. {\textcopyright} 2011 Elsevier Inc.},
author = {Chincarini, Andrea and Bosco, Paolo and Calvini, Piero and Gemme, Gianluca and Esposito, Mario and Olivieri, Chiara and Rei, Luca and Squarcia, Sandro and Rodriguez, Guido and Bellotti, Roberto and Cerello, Piergiorgio and {De Mitri}, Ivan and Retico, Alessandra and Nobili, Flavio},
doi = {10.1016/j.neuroimage.2011.05.083},
issn = {10538119},
journal = {NeuroImage},
keywords = {Alzheimer's disease,Hippocampus,Image analysis,MRI,Medial temporal lobe},
number = {2},
pages = {469--480},
title = {{Local MRI analysis approach in the diagnosis of early and prodromal Alzheimer's disease}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {58},
year = {2011}
}
@article{Choi2019,
abstract = {Objective: A developmental venous anomaly (DVA) is a vascular malformation of ambiguous clinical significance. We aimed to quantify the susceptibility of draining veins ($\chi$vein) in DVA and determine its significance with respect to oxygen metabolism using quantitative susceptibility mapping (QSM). Materials and Methods: Brain magnetic resonance imaging of 27 consecutive patients with incidentally detected DVAs were retrospectively reviewed. Based on the presence of abnormal hyperintensity on T2-weighted images (T2WI) in the brain parenchyma adjacent to DVA, the patients were grouped into edema (E+, n = 9) and non-edema (E-, n = 18) groups. A 3T MR scanner was used to obtain fully flow-compensated gradient echo images for susceptibility-weighted imaging with source images used for QSM processing. The $\chi$vein was measured semi-automatically using QSM. The normalized $\chi$vein was also estimated. Clinical and MR measurements were compared between the E+ and E- groups using Student's t-test or Mann-Whitney U test. Correlations between the $\chi$vein and area of hyperintensity on T2WI and between $\chi$vein and diameter of the collecting veins were assessed. The correlation coefficient was also calculated using normalized veins. Results: The DVAs of the E+ group had significantly higher $\chi$vein (196.5 ± 27.9 vs. 167.7 ± 33.6, p = 0.036) and larger diameter of the draining veins (p = 0.006), and patients were older (p = 0.006) than those in the E- group. The $\chi$vein was also linearly correlated with the hyperintense area on T2WI (r = 0.633, 95{\%} confidence interval 0.333-0.817, p {\textless} 0.001). Conclusion: DVAs with abnormal hyperintensity on T2WI have higher susceptibility values for draining veins, indicating an increased oxygen extraction fraction that might be associated with venous congestion.},
author = {Choi, Yangsean and Jang, Jinhee and Nam, Yoonho and Shin, Na Young and Choi, Hyun Seok and Jung, So Lyung and Ahn, Kook Jin and Kim, Bum Soo},
doi = {10.3348/kjr.2018.0685},
issn = {12296929},
journal = {Korean Journal of Radiology},
keywords = {Developmental venous anomaly,Magnetic resonance imaging,Quantitative susceptibility mapping,Vascular malformation},
number = {4},
pages = {662--670},
title = {{Relationship between abnormal hyperintensity on T2-weighted images around developmental venous anomalies and magnetic susceptibility of their collecting veins: In-vivo quantitative susceptibility mapping study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {20},
year = {2019}
}
@article{Ciardo2017,
abstract = {Objectives Atlas-based automatic segmentation (ABAS) addresses the challenges of accuracy and reliability in manual segmentation. We aim to evaluate the contribution of specific-purpose in ABAS of breast cancer (BC) patients with respect to generic-purpose libraries. Materials and methods One generic-purpose and 9 specific-purpose libraries, stratified according to type of surgery and size of thorax circumference, were obtained from the computed tomography of 200 BC patients. Keywords about contralateral breast volume and presence of breast expander/prostheses were recorded. ABAS was validated on 47 independent patients, considering manual segmentation from scratch as reference. Five ABAS datasets were obtained, testing single-ABAS and multi-ABAS with simultaneous truth and performance level estimation (STAPLE). Center of mass distance (CMD), average Hausdorff distance (AHD) and Dice similarity coefficient (DSC) between corresponding ABAS and manual structures were evaluated and statistically significant differences between different surgeries, structures and ABAS strategies were investigated. Results Statistically significant differences between patients who underwent different surgery were found, with superior results for conservative-surgery group, and between different structures were observed: ABAS of heart, lungs, kidneys and liver was satisfactory (median values: CMD{\textless}2 mm, DSC≥0.80, AHD{\textless}1.5 mm), whereas chest wall, breast and spinal cord obtained moderate performance (median values: 2 mm ≤ CMD{\textless}5 mm, 0.60 ≤ DSC{\textless}0.80, 1.5 mm ≤ AHD{\textless}4 mm) and esophagus, stomach, brachial plexus and supraclavicular nodes obtained poor performance (median CMD≥5 mm, DSC{\textless}0.60, AHD≥4 mm). The application of STAPLE algorithm generally yields higher performance and the use of keywords improves results for breast ABAS. Conclusion The homogeneity in the selection of atlases based on multiple anatomical and clinical features and the use of specific-purpose libraries can improve ABAS performance with respect to generic-purpose libraries.},
author = {Ciardo, Delia and Gerardi, Marianna Alessandra and Vigorito, Sabrina and Morra, Anna and Dell'acqua, Veronica and Diaz, Federico Javier and Cattani, Federica and Zaffino, Paolo and Ricotti, Rosalinda and Spadea, Maria Francesca and Riboldi, Marco and Orecchia, Roberto and Baroni, Guido and Leonardi, Maria Cristina and Jereczek-Fossa, Barbara Alicja},
doi = {10.1016/j.breast.2016.12.010},
issn = {15323080},
journal = {Breast},
keywords = {Atlas-based segmentation,Automatic contouring,Breast cancer radiotherapy,STAPLE contours},
pages = {44--52},
title = {{Atlas-based segmentation in breast cancer radiotherapy: Evaluation of specific and generic-purpose atlases}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {32},
year = {2017}
}
@article{Constanzo2017,
abstract = {With the improvement of external radiotherapy delivery accuracy, such as intensity-modulated and stereotactic body radiation therapy, radiation oncology has recently entered in the era of precision medicine. Despite these precise irradiation modalities, lung cancers remain one of the most aggressive human cancers worldwide, possibly because of diverse genotypic alterations that drive and maintain lung tumorigenesis. It has been long recognized that imaging could aid in the diagnosis, tumor delineation, and monitoring of lung cancer. Moreover, accumulating evidence suggests that imaging information could be further used to tailor treatment type and intensity, as well as predict treatment outcomes in radiotherapy. However, these imaging tasks have been carried out either qualitatively or using simplistic metrics that doesn't take advantage of the full scale of imaging knowledge. Radiomics, which is a recent field of research that aims to provide a more quantitative representation of imaging information relating tumor phenotypes to clinical and genotypic endpoints by embedding extracted image features into predictive mathematical models. These predictive models can be a key component in the clinician decision making and treatment personalization. This review provides an overview of the radiomics application and its methodology for radiation oncology studies in lung cancer.},
author = {Constanzo, Julie and Wei, Lise and Tseng, Huan Hsin and {El Naqa}, Issam},
doi = {10.21037/tlcr.2017.09.07},
issn = {22264477},
journal = {Translational Lung Cancer Research},
keywords = {Biomarkers,Lung cancer,Quantitative imaging,Radiomics},
number = {6},
pages = {635--647},
title = {{Radiomics in precision medicine for lung cancer}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {6},
year = {2017}
}
@article{Conzelmann2011,
abstract = {Cilia-based locomotion is the major form of locomotion for microscopic planktonic organisms in the ocean. Given their negative buoyancy, these organisms must control ciliary activity to maintain an appropriate depth. The neuronal bases of depth regulation in ciliary swimmers are unknown. To gain insights into depth regulation we studied ciliary locomotor control in the planktonic larva of the marine annelid, Platynereis. We found several neuropeptides expressed in distinct sensory neurons that innervate locomotor cilia. Neuropeptides altered ciliary beat frequency and the rate of calcium-evoked ciliary arrests. These changes influenced larval orientation, vertical swimming, and sinking, resulting in upward or downward shifts in the steady-state vertical distribution of larvae. Our findings indicate that Platynereis larvae have depth-regulating peptidergic neurons that directly translate sensory inputs into locomotor output on effector cilia. We propose that the simple circuitry found in these ciliated larvae represents an ancestral state in nervous system evolution.},
author = {Conzelmann, Markus and Offenburger, Sarah Lena and Asadulina, Albina and Keller, Timea and M{\"{u}}nch, Thomas A. and J{\'{e}}kely, G{\'{a}}sp{\'{a}}r},
doi = {10.1073/pnas.1109085108},
issn = {00278424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {FMRFamide-related peptides,Neural circuit,Sensory-motor neuron,Zooplankton},
number = {46},
pages = {E1174--E1183},
title = {{Neuropeptides regulate swimming depth of Platynereis larvae}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {108},
year = {2011}
}
@article{Cordova2016,
abstract = {The diagnosis, prognosis, and management of patients with gliomas are largely dictated by the pathological analysis of tissue biopsied from a selected region within the lesion. However, due to the heterogeneous and infiltrative nature of gliomas, identifying the optimal region for biopsy with conventional magnetic resonance imaging (MRI) can be quite difficult. This is especially true for low grade gliomas, which often are non-enhancing tumors. To improve the management of patients with these tumors, the field of neuro-oncology requires an imaging modality that can specifically identify a tumor's most anaplastic/aggressive region(s) for biopsy targeting. The addition of metabolic mapping using spectroscopic MRI (sMRI) to supplement conventional MRI could improve biopsy targeting and, ultimately, diagnostic accuracy. Here, we describe a pipeline for the integration of state-of-the-art, high-resolution whole-brain 3D sMRI maps into a stereotactic neuronavigation system for guiding biopsies in gliomas with nonenhancing components. We also outline a machine-learning method for automated histology analysis that generates normalized, quantitative metrics describing tumor infiltration in immunohistochemically-stained tissue specimens. As a proof of concept, we describe the combination of these two techniques in a small cohort of grade III glioma patients. In this work, we aim to set forth a systematic pipeline to stimulate histopathology-image validation of advanced MRI techniques, such as sMRI.},
author = {Cordova, J S and Gurbani, S S and Olson, J J and Liang, Z X and Cooper, L A D and Shu, H K G and Schreibmann, E and Neill, S G and Hadjipanayis, C G and Holder, C A and Shim, H},
doi = {10.18383/j.tom.2016.00136},
issn = {2379-1381},
journal = {Tomography},
number = {2},
pages = {106--116},
title = {{A Systematic Pipeline for the Objective Comparison of Whole-Brain Spectroscopic MRI with Histology in Biopsy Specimens from Grade 3 Glioma}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {2},
year = {2016}
}
@article{Cresson2019,
abstract = {Deep learning (DL) techniques are becoming increasingly important to solve a number of image processing tasks. Among common algorithms, convolutional neural network- and recurrent neural network-based systems achieve state-of-the-art results on satellite and aerial imagery in many applications. While these approaches are subject to scientific interest, there is currently no operational and generic implementation available at the user level for the remote sensing (RS) community. In this letter, we present a framework enabling the use of DL techniques with RS images and geospatial data. Our solution takes roots in two extensively used open-source libraries, the RS image processing library Orfeo ToolBox and the high-performance numerical computation library TensorFlow. It can apply deep nets without restriction on image size and is computationally efficient, regardless of hardware configuration.},
archivePrefix = {arXiv},
arxivId = {1807.06535},
author = {Cresson, Remi},
doi = {10.1109/LGRS.2018.2867949},
eprint = {1807.06535},
issn = {15580571},
journal = {IEEE Geoscience and Remote Sensing Letters},
keywords = {Aerial images,Orfeo Toolbox (OTB),TensorFlow (TF),deep learning (DL),neural networks,remote sensing (RS)},
number = {1},
pages = {25--29},
title = {{A framework for remote sensing images processing using deep learning techniques}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {16},
year = {2019}
}
@article{Cresson2016,
abstract = {The amount of remote sensing (RS) data available for applications is constantly growing due to the rise of very high resolution sensors and short-repeat-cycle satellites. Consequently, tackling the computational complexity in Earth observation information extraction is rising as a major challenge. Resorting to high-performance computing (HPC) is becoming a common practice, since this provides environments and programming facilities that are able to speed up processes. In particular, clusters are flexible cost-effective systems that are able to perform data-intensive tasks ideally fulfilling any computational requirement. However, their use typically implies a significant coding effort to build proper implementations of specific processing pipelines. This letter presents a generic framework for the development of RS images processing applications targeting cluster computing. It is based on common open-source libraries and leverages the parallelization of a wide variety of image processing pipelines in a transparent way. Performances on typical RS tasks implemented using the proposed framework demonstrate a great potential for the effective and timely processing of large amount of data.},
archivePrefix = {arXiv},
arxivId = {1609.08893},
author = {Cresson, Remi and Hautreux, Gabriel},
doi = {10.1109/LGRS.2016.2605138},
eprint = {1609.08893},
issn = {1545598X},
journal = {IEEE Geoscience and Remote Sensing Letters},
keywords = {Clusters,Message Passing Interface (MPI),Orfeo ToolBox (OTB),high-performance computing (HPC),parallel computing,remote sensing (RS) image processing},
number = {11},
pages = {1706--1710},
title = {{A generic framework for the development of geospatial processing pipelines on clusters}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84988653888{\&}doi=10.1109{\%}2FLGRS.2016.2605138{\&}partnerID=40{\&}md5=c94a89e4590d599fb89db287d43a4c88},
volume = {13},
year = {2016}
}
@article{2005,
abstract = {We describe a new algorithm for non-rigid registration capable of estimating a constrained dense displacement field from multi-modal image data. We applied this algorithm to capture non-rigid deformation between digital images of histological slides and digital flat-bed scanned images of cryotomed sections of the larynx, and carried out validation experiments to measure the effectiveness of the algorithm. The implementation was carried out by extending the open-source Insight ToolKit software. In diagnostic imaging of cancer of the larynx, imaging modalities sensitive to both anatomy (such as MRI and CT) and function (PET) are valuable. However, these modalities differ in their capability to discriminate the margins of tumor. Gold standard tumor margins can be obtained from histological images from cryotomed sections of the larynx. Unfortunately, the process of freezing, fixation, cryotoming and staining the tissue to create histological images introduces non-rigid deformations and significantcontrast changes. We demonstrate that the non-rigid registration algorithm we present is able to capture these deformations and the algorithm allows us to align histological images with scanned images of the larynx. Our non-rigid registration algorithm constructs a deformation field to warp one image onto another. The algorithm measures image similarity using a mutual information similarity criterion, and avoids spurious deformations due to noise by constraining the estimated deformation field with a linear elastic regularization term. The finite element method is used to represent the deformation field, and our implementation enables us to assign inhomogeneous material characteristics so that hard regions resist internal deformation whereas soft regions are more pliant. A gradient descent optimization strategy is used and this has enabled rapid and accurate convergence to the desired estimate of the deformation field. A further acceleration in speed without cost of accuracy is achieved by using an adaptive mesh refinement strategy. {\textcopyright} 2005 Elsevier B.V. All rights reserved.},
author = {du Bois d'Aische, Aloys and Craene, Mathieu De and Geets, Xavier and Gregoire, Vincent and Macq, Benoit and Warfield, Simon K.},
doi = {10.1016/j.media.2005.04.003},
issn = {13618415},
journal = {Medical Image Analysis},
keywords = {Elastic regularization,ITK,Laryngectomy,Mutual information,Non-rigid registration},
number = {6},
pages = {538--546},
title = {{Efficient multi-modal dense field non-rigid registration: Alignment of histological and section images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {9},
year = {2005}
}
@article{Danelakis2018,
abstract = {Multiple sclerosis (MS) is a chronic disease. It affects the central nervous system and its clinical manifestation can variate. Magnetic Resonance Imaging (MRI) is often used to detect, characterize and quantify MS lesions in the brain, due to the detailed structural information that it can provide. Manual detection and measurement of MS lesions in MRI data is time-consuming, subjective and prone to errors. Therefore, multiple automated methodologies for MRI-based MS lesion segmentation have been proposed. Here, a review of the state-of-the-art of automatic methods available in the literature is presented. The current survey provides a categorization of the methodologies in existence in terms of their input data handling, their main strategy of segmentation and their type of supervision. The strengths and weaknesses of each category are analyzed and explicitly discussed. The positive and negative aspects of the methods are highlighted, pointing out the future trends and, thus, leading to possible promising directions for future research. In addition, a further clustering of the methods, based on the databases used for their evaluation, is provided. The aforementioned clustering achieves a reliable comparison among methods evaluated on the same databases. Despite the large number of methods that have emerged in the field, there is as yet no commonly accepted methodology that has been established in clinical practice. Future challenges such as the simultaneous exploitation of more sophisticated MRI protocols and the hybridization of the most promising methods are expected to further improve the performance of the segmentation.},
author = {Danelakis, Antonios and Theoharis, Theoharis and Verganelakis, Dimitrios A.},
doi = {10.1016/j.compmedimag.2018.10.002},
issn = {18790771},
journal = {Computerized Medical Imaging and Graphics},
keywords = {Automated segmentation,Brain MRI,Multiple sclerosis,Survey},
pages = {83--100},
title = {{Survey of automated multiple sclerosis lesion segmentation techniques on magnetic resonance imaging}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {70},
year = {2018}
}
@inproceedings{Danilov,
abstract = {The work addresses segmentation techniques for generation of individualized computational domains on the basis of medical imaging dataset. The computational domains will be used in 3D electrophysiology models and 3D-1D coupled hemodynamics models. Several techniques for user-guided and automated segmentation of soft tissues, segmentation of vascular and tubular structures, generation of centerlines, 1D network reconstruction, correction and local adaptation are examined. We propose two algorithms for automatic vascular network segmentation and user-guided cardiac segmentation.},
author = {Danilov, Alexander A. and Pryamonosov, Roman A. and Yurova, Alexandra S.},
booktitle = {ECCOMAS Congress 2016 - Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering},
doi = {10.7712/100016.1827.10770},
isbn = {9786188284401},
keywords = {Electrophysiology,Hemodynamics,Image segmentation,Medical images,Mesh generation},
pages = {454--461},
title = {{Image segmentation techniques for biomedical modeling: Electrophysiology and hemodynamics}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995513594{\&}doi=10.7712{\%}2F100016.1827.10770{\&}partnerID=40{\&}md5=3954727a98c6ce96faf2f9cd25fba8f7},
volume = {1},
year = {2016}
}
@article{Datta2018,
abstract = {An increasing demand for directed assembly of biomaterials has inspired the development of bioprinting, which facilitates the assembling of both cellular and acellular inks into well-arranged three-dimensional (3D) structures for tissue fabrication. Although great advances have been achieved in the recent decade, there still exist issues to be addressed. Herein, a review has been systematically performed to discuss the considerations in the entire procedure of bioprinting. Though bioprinting is advancing at a rapid pace, it is seen that the whole process of obtaining tissue constructs from this technique involves multiple-stages, cutting across various technology domains. These stages can be divided into three broad categories: pre-bioprinting, bioprinting and post-bioprinting. Each stage can influence others and has a bearing on the performance of fabricated constructs. For example, in pre-bioprinting, tissue biopsy and cell expansion techniques are essential to ensure a large number of cells are available for mass organ production. Similarly, medical imaging is needed to provide high resolution designs, which can be faithfully bioprinted. In the bioprinting stage, compatibility of biomaterials is needed to be matched with solidification kinetics to ensure constructs with high cell viability and fidelity are obtained. On the other hand, there is a need to develop bioprinters, which have high degrees of freedom of movement, perform without failure concerns for several hours and are compact, and affordable. Finally, maturation of bioprinted cells are governed by conditions provided during the post-bioprinting process. This review, for the first time, puts all the bioprinting stages in perspective of the whole process of bioprinting, and analyzes their current state-of-the art. It is concluded that bioprinting community will recognize the relative importance and optimize the parameter of each stage to obtain the desired outcomes.},
author = {Datta, Pallab and Barui, Ananya and Wu, Yang and Ozbolat, Veli and Moncal, Kazim K. and Ozbolat, Ibrahim T.},
doi = {10.1016/j.biotechadv.2018.06.003},
issn = {07349750},
journal = {Biotechnology Advances},
keywords = {Biofabrication,Bioink,Bioprinter,Bioprinting,Droplet-based bioprinting,Extrusion-based bioprinting,Laser-based bioprinting},
number = {5},
pages = {1481--1504},
pmid = {29909085},
title = {{Essential steps in bioprinting: From pre- to post-bioprinting}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {36},
year = {2018}
}
@article{DeBellis2015,
abstract = {Magnetic resonance imaging studies of maltreated children with posttraumatic stress disorder (PTSD) suggest that maltreatment-related PTSD is associated with adverse brain development. Maltreated youth resilient to chronic PTSD were not previously investigated and may elucidate neuromechanisms of the stress diathesis that leads to resilience to chronic PTSD. In this cross-sectional study, anatomical volumetric and corpus callosum diffusion tensor imaging measures were examined using magnetic resonance imaging in maltreated youth with chronic PTSD (N = 38), without PTSD (N = 35), and nonmaltreated participants (n = 59). Groups were sociodemographically similar. Participants underwent assessments for strict inclusion/exclusion criteria and psychopathology. Maltreated youth with PTSD were psychobiologically different from maltreated youth without PTSD and nonmaltreated controls. Maltreated youth with PTSD had smaller posterior cerebral and cerebellar gray matter volumes than did maltreated youth without PTSD and nonmaltreated participants. Cerebral and cerebellar gray matter volumes inversely correlated with PTSD symptoms. Posterior corpus callosum microstructure in pediatric maltreatment-related PTSD differed compared to maltreated youth without PTSD and controls. The group differences remained significant when controlling for psychopathology, numbers of Axis I disorders, and trauma load. Alterations of these posterior brain structures may result from a shared trauma-related mechanism or an inherent vulnerability that mediates the pathway from chronic PTSD to comorbidity.},
author = {{De Bellis}, Michael D. and Hooper, Stephen R. and Chen, Steven D. and Provenzale, James M. and Boyd, Brian D. and Glessner, Christopher E. and Macfall, James R. and Payne, Martha E. and Rybczynski, Robert and Woolley, Donald P.},
doi = {10.1017/S0954579415000942},
issn = {14692198},
journal = {Development and Psychopathology},
number = {4},
pages = {1555--1576},
title = {{Posterior structural brain volumes differ in maltreated youth with and without chronic posttraumatic stress disorder}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {27},
year = {2015}
}
@article{DeLeener2015,
abstract = {Quantifying spinal cord (SC) atrophy in neurodegenerative and traumatic diseases brings important diagnosis and prognosis information for the clinician. We recently developed the PropSeg method, which allows for fast, accurate and automatic segmentation of the SC on different types of MRI contrast (e.g.,T1-,T2-and T2z.ast;-weighted sequences) and any field of view. However, comparing measurements from the SC between subjects is hindered by the lack of a generic coordinate system for the SC. In this paper, we present a new framework combining PropSeg and a vertebral level identification method, thereby enabling direct inter-and intra-subject comparison of SC measurements for large cohort studies as well as for longitudinal studies. Our segmentation method is based on the multi-resolution propagation of tubular deformable models. Coupled with an automatic intervertebral disk identification method, our segmentation pipeline provides quantitative metrics of the SC and spinal canal such as cross-sectional areas and volumes in a generic coordinate system based on vertebral levels. This framework was validated on 17 healthy subjects and on one patient with SC injury against manual segmentation. Results have been compared with an existing active surface method and show high local and global accuracy for both SC and spinal canal (Dice coefficients =0.91 ± 0.02) segmentation. Having a robust and automatic framework for SC segmentation and vertebral-based normalization opens the door to bias-free measurement of SC atrophy in large cohorts.},
author = {{De Leener}, Benjamin and Cohen-Adad, Julien and Kadoury, Samuel},
doi = {10.1109/TMI.2015.2437192},
issn = {1558254X},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Automatic segmentation,CSF,MRI,Vertebral labeling,deformable model,spinal canal,spinal cord},
number = {8},
pages = {1705--1718},
title = {{Automatic Segmentation of the Spinal Cord and Spinal Canal Coupled with Vertebral Labeling}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {34},
year = {2015}
}
@article{DeLeener2014,
abstract = {Spinal cord segmentation provides measures of atrophy and facilitates group analysis via inter-subject correspondence. Automatizing this procedure enables studies with large throughput and minimizes user bias. Although several automatic segmentation methods exist, they are often restricted in terms of image contrast and field-of-view. This paper presents a new automatic segmentation method (PropSeg) optimized for robustness, accuracy and speed. The algorithm is based on the propagation of a deformable model and is divided into three parts: firstly, an initialization step detects the spinal cord position and orientation using a circular Hough transform on multiple axial slices rostral and caudal to the starting plane and builds an initial elliptical tubular mesh. Secondly, a low-resolution deformable model is propagated along the spinal cord. To deal with highly variable contrast levels between the spinal cord and the cerebrospinal fluid, the deformation is coupled with a local contrast-to-noise adaptation at each iteration. Thirdly, a refinement process and a global deformation are applied on the propagated mesh to provide an accurate segmentation of the spinal cord. Validation was performed in 15 healthy subjects and two patients with spinal cord injury, using T1- and T2-weighted images of the entire spinal cord and on multiecho T2*-weighted images. Our method was compared against manual segmentation and against an active surface method. Results show high precision for all the MR sequences. Dice coefficients were 0.9 for the T1- and T2-weighted cohorts and 0.86 for the T2*-weighted images. The proposed method runs in less than 1min on a normal computer and can be used to quantify morphological features such as cross-sectional area along the whole spinal cord. {\textcopyright} 2014 Elsevier Inc.},
author = {{De Leener}, Benjamin and Kadoury, Samuel and Cohen-Adad, Julien},
doi = {10.1016/j.neuroimage.2014.04.051},
issn = {10959572},
journal = {NeuroImage},
keywords = {Automatic,Deformable model,MRI,Propagation,Spinal cord segmentation},
pages = {528--536},
title = {{Robust, accurate and fast automatic segmentation of the spinal cord}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {98},
year = {2014}
}
@article{DeLeener2017,
abstract = {For the past 25 years, the field of neuroimaging has witnessed the development of several software packages for processing multi-parametric magnetic resonance imaging (mpMRI) to study the brain. These software packages are now routinely used by researchers and clinicians, and have contributed to important breakthroughs for the understanding of brain anatomy and function. However, no software package exists to process mpMRI data of the spinal cord. Despite the numerous clinical needs for such advanced mpMRI protocols (multiple sclerosis, spinal cord injury, cervical spondylotic myelopathy, etc.), researchers have been developing specific tools that, while necessary, do not provide an integrative framework that is compatible with most usages and that is capable of reaching the community at large. This hinders cross-validation and the possibility to perform multi-center studies. In this study we introduce the Spinal Cord Toolbox (SCT), a comprehensive software dedicated to the processing of spinal cord MRI data. SCT builds on previously-validated methods and includes state-of-the-art MRI templates and atlases of the spinal cord, algorithms to segment and register new data to the templates, and motion correction methods for diffusion and functional time series. SCT is tailored towards standardization and automation of the processing pipeline, versatility, modularity, and it follows guidelines of software development and distribution. Preliminary applications of SCT cover a variety of studies, from cross-sectional area measures in large databases of patients, to the precise quantification of mpMRI metrics in specific spinal pathways. We anticipate that SCT will bring together the spinal cord neuroimaging community by establishing standard templates and analysis procedures.},
author = {{De Leener}, Benjamin and L{\'{e}}vy, Simon and Dupont, Sara M. and Fonov, Vladimir S. and Stikov, Nikola and {Louis Collins}, D. and Callot, Virginie and Cohen-Adad, Julien},
doi = {10.1016/j.neuroimage.2016.10.009},
issn = {10959572},
journal = {NeuroImage},
keywords = {Atlas,MRI,Open-source,Software,Spinal cord,Template},
pages = {24--43},
title = {{SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {145},
year = {2017}
}
@article{Reuille2015,
abstract = {Morphogenesis emerges from complex multiscale interactions between genetic and mechanical processes. To understand these processes, the evolution of cell shape, proliferation and gene expression must be quantified. This quantification is usually performed either in full 3D, which is computationally expensive and technically challenging, or on 2D planar projections, which introduces geometrical artifacts on highly curved organs. Here we present MorphoGraphX (www.MorphoGraphX.org), a software that bridges this gap by working directly with curved surface images extracted from 3D data. In addition to traditional 3D image analysis, we have developed algorithms to operate on curved surfaces, such as cell segmentation, lineage tracking and fluorescence signal quantification. The software's modular design makes it easy to include existing libraries, or to implement new algorithms. Cell geometries extracted with MorphoGraphX can be exported and used as templates for simulation models, providing a powerful platform to investigate the interactions between shape, genes and growth.},
author = {de Reuille, Pierre Barbier and Routier-Kierzkowska, Anne Lise and Kierzkowski, Daniel and Bassel, George W. and Sch{\"{u}}pbach, Thierry and Tauriello, Gerardo and Bajpai, Namrata and Strauss, S{\"{o}}ren and Weber, Alain and Kiss, Annamaria and Burian, Agata and Hofhuis, Hugo and Sapala, Aleksandra and Lipowczan, Marcin and Heimlicher, Maria B. and Robinson, Sarah and Bayer, Emmanuelle M. and Basler, Konrad and Koumoutsakos, Petros and Roeder, Adrienne H.K. and Aegerter-Wilmsen, Tinri and Nakayama, Naomi and Tsiantis, Miltos and Hay, Angela and Kwiatkowska, Dorota and Xenarios, Ioannis and Kuhlemeier, Cris and Smith, Richard S.},
doi = {10.7554/eLife.05864},
issn = {2050084X},
journal = {eLife},
number = {MAY},
pages = {1--20},
pmid = {25946108},
title = {{MorphoGraphX: A platform for quantifying morphogenesis in 4D}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {4},
year = {2015}
}
@article{DeSchryver2018,
abstract = {This work presents a framework to exploit the synergy between Digital Volume Correlation (DVC) and iterative CT reconstruction to enhance the quality of high-resolution dynamic X-ray CT (4D-$\mu$CT) and obtain quantitative results from the acquired dataset in the form of 3D strain maps which can be directly correlated to the material properties. Furthermore, we show that the developed framework is capable of strongly reducing motion artifacts even in a dataset containing a single 360° rotation.},
author = {{De Schryver}, Thomas and Dierick, Manuel and Heyndrickx, Marjolein and {Van Stappen}, Jeroen and Boone, Marijn A. and {Van Hoorebeke}, Luc and Boone, Matthieu N.},
doi = {10.1038/s41598-018-25916-5},
issn = {20452322},
journal = {Scientific Reports},
number = {1},
pages = {10},
title = {{Motion compensated micro-CT reconstruction for in-situ analysis of dynamic processes}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {8},
year = {2018}
}
@article{Fresno2009,
abstract = {Image segmentation of 3D medical images is a challenging problem with several still not totally solved practical issues, such as noise interference, variable object structures and image artifacts. This paper describes a hybrid 3D image segmentation method which combines region growing and deformable models to obtain accurate and topologically preserving surface structures of anatomical objects of interest. The proposed strategy starts by determining a rough but robust approximation of the objects using a region-growing algorithm. Then, the closed surface mesh that encloses the region is constructed and used as the initial geometry of a deformable model for the final refinement. This integrated strategy provides an alternative solution to one of the flaws of traditional deformable models, achieving good refinements of internal surfaces in few steps. Experimental segmentation results of complex anatomical structures on both simulated and real data from MRI scans are presented, and the method is assessed by comparing with standard reference segmentations of head MRI. The evaluation was mainly based on the average overlap measure, which was tested on the segmentation of white matter, corresponding to a simulated brain data set, showing excellent performance exceeding 90{\%} accuracy. In addition, the algorithm was applied to the detection of anatomical head structures on two real MRI and one CT data set. The final reconstructions resulting from the deformable models produce high quality meshes suitable for 3D visualization and further numerical analysis. The obtained results show that the approach achieves high quality segmentations with low computational complexity. {\textcopyright} 2009 Elsevier Ltd. All rights reserved.},
author = {del Fresno, M. and V{\'{e}}nere, M. and Clausse, A.},
doi = {10.1016/j.compmedimag.2009.03.002},
issn = {08956111},
journal = {Computerized Medical Imaging and Graphics},
keywords = {Deformable surface models,Hybrid methods,Image segmentation,MRI,Region growing},
number = {5},
pages = {369--376},
title = {{A combined region growing and deformable model method for extraction of closed surfaces in 3D CT and MRI scans}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-67349195754{\&}doi=10.1016{\%}2Fj.compmedimag.2009.03.002{\&}partnerID=40{\&}md5=93b30aa2fbeb65fa285de2c2c00f07fc},
volume = {33},
year = {2009}
}
@article{Deng2016,
abstract = {Purpose: Segmentation of brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is crucial for brain structural measurement and disease diagnosis. Learning-based segmentation methods depend largely on the availability of good training ground truth. However, the commonly used 3T MR images are of insufficient image quality and often exhibit poor intensity contrast between WM, GM, and CSF. Therefore, they are not ideal for providing good ground truth label data for training learning-based methods. Recent advances in ultrahigh field 7T imaging make it possible to acquire images with excellent intensity contrast and signal-to-noise ratio. Methods: In this paper, the authors propose an algorithm based on random forest for segmenting 3T MR images by training a series of classifiers based on reliable labels obtained semiautomatically from 7T MR images. The proposed algorithm iteratively refines the probability maps of WM, GM, and CSF via a cascade of random forest classifiers for improved tissue segmentation. Results: The proposed method was validated on two datasets, i.e., 10 subjects collected at their institution and 797 3T MR images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. Specifically, for the mean Dice ratio of all 10 subjects, the proposed method achieved 94.52{\%} ± 0.9{\%}, 89.49{\%} ± 1.83{\%}, and 79.97{\%} ± 4.32{\%} for WM, GM, and CSF, respectively, which are significantly better than the state-of-the-art methods (p-values {\textless} 0.021). For the ADNI dataset, the group difference comparisons indicate that the proposed algorithm outperforms state-of-the-art segmentation methods. Conclusions: The authors have developed and validated a novel fully automated method for 3T brain MR image segmentation.},
author = {Deng, Minghui and Yu, Renping and Wang, Li and Shi, Feng and Yap, Pew Thian and Shen, Dinggang},
doi = {10.1118/1.4967487},
issn = {00942405},
journal = {Medical Physics},
number = {12},
pages = {6588--6597},
title = {{Learning-based 3T brain MRI segmentation with guidance from 7T MRI labeling}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2016}
}
@article{Dietrich2014,
abstract = {Composite sandwich structures with honeycomb cores show varying properties in geometry and mechanical behavior depending on the studied scale. Herein a new test and evaluation method for sub-surface core damage in the indentation area of honeycomb sandwich structures using computed tomography is presented. The combination of X-ray micro-computed tomography (X-$\mu$CT) and an image analysis procedure adjusted to the detection of core deformation mechanisms allows the extraction and quantification of externally invisible, sub-surface damage in the sandwich composite. For this specific contact or indentation loading case on the sandwich face sheet an in-situ device is introduced, enabling a 3D analysis of the structural change during progressing indentation depth.},
author = {Dietrich, S. and Koch, M. and Elsner, P. and Weidenmann, K.},
doi = {10.1007/s11340-014-9902-2},
issn = {17412765},
journal = {Experimental Mechanics},
keywords = {Contact modelling,GFRP honeycomb sandwich,In-situ loading,Indentation,Micro-computed tomography},
number = {8},
pages = {1385--1393},
title = {{Measurement of Sub-Surface Core Damage in Sandwich Structures Using In-situ Hertzian Indentation During X-ray Computed Tomography}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {54},
year = {2014}
}
@article{Dolly2018,
abstract = {It is widely known that the optimization of imaging systems based on objective, task-based measures of image quality via computer-simulation requires the use of a stochastic object model (SOM). However, the development of computationally tractable SOMs that can accurately model the statistical variations in human anatomy within a specified ensemble of patients remains a challenging task. Previously reported numerical anatomic models lack the ability to accurately model inter-patient and inter-organ variations in human anatomy among a broad patient population, mainly because they are established on image data corresponding to a few of patients and individual anatomic organs. This may introduce phantom-specific bias into computer-simulation studies, where the study result is heavily dependent on which phantom is used. In certain applications, however, databases of high-quality volumetric images and organ contours are available that can facilitate this SOM development. In this work, a novel and tractable methodology for learning a SOM and generating numerical phantoms from a set of volumetric training images is developed. The proposed methodology learns geometric attribute distributions (GAD) of human anatomic organs from a broad patient population, which characterize both centroid relationships between neighboring organs and anatomic shape similarity of individual organs among patients. By randomly sampling the learned centroid and shape GADs with the constraints of the respective principal attribute variations learned from the training data, an ensemble of stochastic objects can be created. The randomness in organ shape and position reflects the learned variability of human anatomy. To demonstrate the methodology, a SOM of an adult male pelvis is computed and examples of corresponding numerical phantoms are created.},
author = {Dolly, Steven R. and Lou, Yang and Anastasio, Mark A. and Li, Hua},
doi = {10.1088/1361-6560/aab000},
issn = {13616560},
journal = {Physics in Medicine and Biology},
number = {6},
pages = {18},
title = {{Learning-based stochastic object models for characterizing anatomical variations}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {63},
year = {2018}
}
@article{Dolly2019,
abstract = {In the majority of current radiation therapy (RT) applications, image quality is still assessed subjectively or by utilizing physical measures. A novel theory that applies objective task-based image quality assessment in radiation therapy (IQA-in-RT) was recently proposed, in which the area under the therapeutic operating characteristic curve (AUTOC) was employed as the figure-of-merit (FOM) for evaluating RT effectiveness. Although theoretically more appealing than conventional subjective or physical measures, a comprehensive implementation and evaluation of this novel task-based IQA-in-RT theory is required for its further application in improving clinical RT. In this work, a practical and modular IQA-in-RT framework is presented for implementing this theory for the assessment of imaging components on the basis of RT treatment outcomes. Computer-simulation studies are conducted to demonstrate the feasibility and utility of the proposed IQA-in-RT framework in optimizing x-ray computed tomography (CT) pre-treatment imaging, including the optimization of CT imaging dose and image reconstruction parameters. The potential advantages of optimizing imaging components in the RT workflow by use of the AUTOC as the FOM are also compared against those of other physical measures. The results demonstrate that optimization using the AUTOC leads to selecting different parameters from those indicated by physical measures, potentially improving RT performance. The sources of systemic randomness and bias that affect the determination of the AUTOC are also analyzed. The presented work provides a practical solution for the further investigation and analysis of the task-based IQA-in-RT theory and advances its applications in improving RT clinical practice and cancer patient care.},
author = {Dolly, Steven R. and Lou, Yang and Anastasio, Mark A. and Li, Hua},
doi = {10.1088/1361-6560/ab2dc5},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {geometric attribute distribution model,learning-based stochastic object models,radiation therapy,task-based image quality assessment,therapeutic operating characteristic curve},
number = {14},
pages = {19},
title = {{Task-based image quality assessment in radiation therapy: Initial characterization and demonstration with computer-simulation study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {64},
year = {2019}
}
@article{Dolz2016,
abstract = {Purpose: Accurate delineation of organs at risk (OARs) on computed tomography (CT) image is required for radiation treatment planning (RTP). Manual delineation of OARs being time consuming and prone to high interobserver variability, many (semi-) automatic methods have been proposed. However, most of them are specific to a particular OAR. Here, an interactive computer-assisted system able to segment various OARs required for thoracic radiation therapy is introduced. Methods: Segmentation information (foreground and background seeds) is interactively added by the user in any of the three main orthogonal views of the CT volume and is subsequently propagated within the whole volume. The proposed method is based on the combination of watershed transformation and graph-cuts algorithm, which is used as a powerful optimization technique to minimize the energy function. The OARs considered for thoracic radiation therapy are the lungs, spinal cord, trachea, proximal bronchus tree, heart, and esophagus. The method was evaluated on multivendor CT datasets of 30 patients. Two radiation oncologists participated in the study and manual delineations from the original RTP were used as ground truth for evaluation. Results: Delineation of the OARs obtained with the minimally interactive approach was approved to be usable for RTP in nearly 90{\%} of the cases, excluding the esophagus, which segmentation was mostly rejected, thus leading to a gain of time ranging from 50{\%} to 80{\%} in RTP. Considering exclusively accepted cases, overall OARs, a Dice similarity coefficient higher than 0.7 and a Hausdorff distance below 10 mm with respect to the ground truth were achieved. In addition, the interobserver analysis did not highlight any statistically significant difference, at the exception of the segmentation of the heart, in terms of Hausdorff distance and volume difference. Conclusions: An interactive, accurate, fast, and easy-to-use computer-assisted system able to segment various OARs required for thoracic radiation therapy has been presented and clinically evaluated. The introduction of the proposed system in clinical routine may offer valuable new option to radiation oncologists in performing RTP.},
author = {Dolz, J. and Kirişli, H. A. and Fechter, T. and Karnitzki, S. and Oehlke, O. and Nestle, U. and Vermandel, M. and Massoptier, L.},
doi = {10.1118/1.4947484},
issn = {00942405},
journal = {Medical Physics},
keywords = {autocontouring,lung cancer,organs at risk segmentation,radiotherapy,thoracic oncology,treatment planning},
number = {5},
pages = {2569--2580},
title = {{Interactive contour delineation of organs at risk in radiotherapy: Clinical evaluation on NSCLC patients}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2016}
}
@article{Dominguez2017,
abstract = {Background and Objective. The effective processing of biomedical images usually requires the interoperability of diverse software tools that have different aims but are complementary. The goal of this work is to develop a bridge to connect two of those tools: ImageJ, a program for image analysis in life sciences, and OpenCV, a computer vision and machine learning library. Methods. Based on a thorough analysis of ImageJ and OpenCV, we detected the features of these systems that could be enhanced, and developed a library to combine both tools, taking advantage of the strengths of each system. The library was implemented on top of the SciJava converter framework. We also provide a methodology to use this library. Results. We have developed the publicly available library IJ-OpenCV that can be employed to create applications combining features from both ImageJ and OpenCV. From the perspective of ImageJ developers, they can use IJ-OpenCV to easily create plugins that use any functionality provided by the OpenCV library and explore different alternatives. From the perspective of OpenCV developers, this library provides a link to the ImageJ graphical user interface and all its features to handle regions of interest. Conclusions. The IJ-OpenCV library bridges the gap between ImageJ and OpenCV, allowing the connection and the cooperation of these two systems.},
author = {Dom{\'{i}}nguez, C{\'{e}}sar and Heras, J{\'{o}}nathan and Pascual, Vico},
doi = {10.1016/j.compbiomed.2017.03.027},
issn = {18790534},
journal = {Computers in Biology and Medicine},
keywords = {Biomedicine,Computer vision,Image processing,ImageJ,Interoperability,Machine learning,OpenCV},
pages = {189--194},
title = {{IJ-OpenCV: Combining ImageJ and OpenCV for processing images in biomedicine}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {84},
year = {2017}
}
@article{BoisdAische2007,
abstract = {We present a new non-rigid registration algorithm estimating the displacement field generated by articulated bodies. Indeed the bony structures between different patient images may rigidly move while other tissues may deform in a more complex way. Our algorithm tracks the displacement induced in the column by a movement of the patient between two acquisitions. The volumetric deformation field in the whole body is then inferred from those displacements using a linear elastic biomechanical finite element model. We demonstrate in this paper that this method provides accurate results on 3D sets of computed tomography (CT), MR and positron emission tomography (PET) images and that the results of the registration algorithm show significant decreases in the mean, min and max errors. {\textcopyright} 2007 Elsevier Ltd. All rights reserved.},
author = {du Bois d'Aische, Aloys and {De Craene}, Mathieu and Geets, Xavier and Gr{\'{e}}goire, Vincent and Macq, Benoit and Warfield, Simon K.},
doi = {10.1016/j.bspc.2007.03.002},
issn = {17468094},
journal = {Biomedical Signal Processing and Control},
keywords = {Articulation,Registration,Vertebrae},
number = {1},
pages = {16--24},
title = {{Estimation of the deformations induced by articulated bodies: Registration of the spinal column}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-34248594102{\&}doi=10.1016{\%}2Fj.bspc.2007.03.002{\&}partnerID=40{\&}md5=845c913387e22bc8438d1efee07c729f},
volume = {2},
year = {2007}
}
@article{Dziubek2016,
abstract = {A computational model for retinal hemodynamics accounting for ocular curvature is presented. The model combines (i) a hierarchical Darcy model for the flow through small arterioles, capillaries and small venules in the retinal tissue, where blood vessels of different size are comprised in different hierarchical levels of a porous medium; and (ii) a one-dimensional network model for the blood flow through retinal arterioles and venules of larger size. The non-planar ocular shape is included by (i) defining the hierarchical Darcy flow model on a two-dimensional curved surface embedded in the three-dimensional space; and (ii) mapping the simplified one-dimensional network model onto the curved surface. The model is solved numerically using a finite element method in which spatial domain and hierarchical levels are discretized separately. For the finite element method, we use an exterior calculus-based implementation which permits an easier treatment of non-planar domains. Numerical solutions are verified against suitably constructed analytical solutions. Numerical experiments are performed to investigate how retinal hemodynamics is influenced by the ocular shape (sphere, oblate spheroid, prolate spheroid and barrel are compared) and vascular architecture (four vascular arcs and a branching vascular tree are compared). The model predictions show that changes in ocular shape induce non-uniform alterations of blood pressure and velocity in the retina. In particular, we found that (i) the temporal region is affected the least by changes in ocular shape, and (ii) the barrel shape departs the most from the hemispherical reference geometry in terms of associated pressure and velocity distributions in the retinal microvasculature. These results support the clinical hypothesis that alterations in ocular shape, such as those occurring in myopic eyes, might be associated with pathological alterations in retinal hemodynamics.},
author = {Dziubek, Andrea and Guidoboni, Giovanna and Harris, Alon and Hirani, Anil N. and Rusjan, Edmond and Thistleton, William},
doi = {10.1007/s10237-015-0731-8},
issn = {16177940},
journal = {Biomechanics and Modeling in Mechanobiology},
keywords = {Finite element exterior calculus,Hierarchical porous medium,Mathematical modeling,Ocular curvature,Retinal hemodynamics,Vascular network},
number = {4},
pages = {893--907},
title = {{Effect of ocular shape and vascular geometry on retinal hemodynamics: a computational model}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {15},
year = {2016}
}
@inbook{ElNaqa2016,
abstract = {PET imaging is a main diagnostic modality of different diseases including cancer. In the particular case of cancer, PET is widely used for staging of disease progression, identification of the treatment gross tumor volume, monitoring of disease, as well as prediction of outcomes and personalization of treatment regimens. Among the arsenal of different functional imaging modalities, PET has benefited from early adoption of quantitative image analysis starting from simple standard uptake value (SUV) normalization to more advanced extraction of complex imaging uptake patterns, thanks chiefly to the application of sophisticated image processing algorithms. In this chapter, we discuss the application of image processing techniques to PET imaging with special focus on the oncological radiotherapy domain starting from basic feature extraction to application in target definition using image segmentation/registration and more recent image-based outcome modeling in the radiomics field. We further extend the discussion into hybrid anatomical functional combinations of PET/CT and PET/MR multimodalities.},
author = {{El Naqa}, Issam},
booktitle = {Basic Science of PET Imaging},
doi = {10.1007/978-3-319-40070-9_12},
isbn = {9783319400709},
keywords = {Hybrid imaging,Image processing,Quantitative PET,Radiomics},
pages = {285--301},
title = {{Image processing and analysis of PET and hybrid PET imaging}},
type = {Book Section},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018895187{\&}doi=10.1007{\%}2F978-3-319-40070-9{\_}12{\&}partnerID=40{\&}md5=a2572f421a96f250c396c7c0538f189d},
year = {2016}
}
@article{Ertz2014,
author = {Ertz, Olivier and Rey, Sergio J. and Joost, St{\'{e}}phane},
doi = {10.5311/JOSIS.2014.8.182},
issn = {1948660X},
journal = {Journal of Spatial Information Science},
number = {2014},
pages = {67--71},
title = {{The open source dynamics in geospatial research and education}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919960299{\&}doi=10.5311{\%}2FJOSIS.2014.8.182{\&}partnerID=40{\&}md5=a7c671f5acc3d9aeaea0fe07a547c7fb},
volume = {8},
year = {2014}
}
@article{Esposito2011,
abstract = {In this paper we present the development of a software for the extraction of the hippocampus and surrounding medial-temporal-lobe (MTL) regions from T1-weighted magnetic resonance (MR) and from Positron Emission Tomography (PET) images with no interactive input from the user. With this software we introduce a novel statistical index computed on the intensities in the automatically extracted MTL regions. This index is a measure of gray-matter (GM) atrophy and allows to: distinguish between (a) patients with Alzheimer's disease (AD), patients with amnestic mild cognitive impairment (aMCI), (b) patients with amnestic mild cognitive impairment who will later develop AD in a time frame of 2 years (aMCIconv), and (c) a set of age-matched elderly controls. Once refined, this method could be used to infer about the clinical outcome of aMCI patients. PACS 87.57.nj-Registration. PACS 87.57.nm-Segmentation. PACS 87.57.R-Computer-aided diagnosis. PACS 87.61.Tg-Clinical applications. {\textcopyright} Societ{\`{a}} Italiana di Fisica.},
author = {Esposito, M. and Bosco, P. and Rei, L. and Aiello, M.},
doi = {10.1393/ncc/i2011-10807-0},
issn = {18269885},
journal = {Nuovo Cimento della Societa Italiana di Fisica C},
number = {1},
pages = {175--185},
title = {{Volumetric analysis on MRI and PET images for the early diagnosis of Alzheimer's disease}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870415031{\&}doi=10.1393{\%}2Fncc{\%}2Fi2011-10807-0{\&}partnerID=40{\&}md5=56add27b1e0232de26da65f2420fcbc5},
volume = {34},
year = {2011}
}
@article{Fallavollita2010,
abstract = {CARTO XP is an electroanatomical cardiac mapping system that provides 3D color-coded maps of the electrical activity of the heart; however it is expensive and it can only use a single costly magnetic catheter for each patient intervention. Our approach consists of integrating fluoroscopic and electrical data from the RF catheters into the same image so as to better guide RF ablation, shorten the duration of this procedure, increase its efficacy, and decrease hospital cost when compared to CARTO XP. We propose a method that relies on multi-view C-arm fluoroscopy image acquisition for (1) the 3D reconstruction of the anatomical structure of interest, (2) the robust temporal tracking of the tip-electrode of a mapping catheter between the diastolic and systolic phases and (3) the 2D/3D registration of color coded isochronal maps directly on the 2D fluoroscopy image that would help the clinician guide the ablation procedure much more effectively. The method has been tested on canine experimental data. {\textcopyright} 2010 Pascal Fallavollita.},
author = {Fallavollita, Pascal},
doi = {10.1155/2010/871409},
issn = {16875176},
journal = {Eurasip Journal on Image and Video Processing},
pages = {10},
title = {{Acquiring multiview C-arm images to assist cardiac ablation procedures}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {2010},
year = {2010}
}
@article{Fallavollita2010a,
abstract = {Purpose: In prostate brachytherapy, transrectal ultrasound (TRUS) is used to visualize the anatomy, while implanted seeds can be visualized by fluoroscopy. Intraoperative dosimetry optimization is possible using a combination of TRUS and fluoroscopy, but requires localization of the fluoroscopy-derived seed cloud, relative to the anatomy as seen on TRUS. The authors propose to develop a method of registration of TRUS images and the implants reconstructed from fluoroscopy. Methods: A phantom was implanted with 48 seeds then imaged with TRUS and CT. Seeds were reconstructed from CT yielding a cloud of seeds. Fiducial-based ground-truth registration was established between the TRUS and CT. TRUS images are filtered, compounded, and registered to the reconstructed implants by using an intensity-based metric. The authors evaluated a volume-to-volume and point-to-volume registration scheme. In total, seven TRUS filtering techniques and three image similarity metrics were analyzed. The method was also tested on human subject data captured from a brachytherapy procedure. Results: For volume-to-volume registration, noise reduction filter and normalized correlation metrics yielded the best result: An average of 0.54±0.11 mm seed localization error relative to ground truth. For point-to-volume registration, noise reduction combined with beam profile filter and mean squares metrics yielded the best result: An average of 0.38±0.19 mm seed localization error relative to the ground truth. In human patient data, C-arm fluoroscopy images showed 81 radioactive seeds implanted inside the prostate. A qualitative analysis showed clinically correct agreement between the seeds visible in TRUS and reconstructed from intraoperative fluoroscopy imaging. The measured registration error compared to the manually selected seed locations by the clinician was 2.86±1.26 mm. Conclusions: Fully automated registration between TRUS and the reconstructed seeds performed well in ground-truth phantom experiments and qualitative observation showed adequate performance on early clinical patient data. {\textcopyright} 2010 American Association of Physicists in Medicine.},
author = {Fallavollita, P. and {Karim Aghaloo}, Z. and Burdette, E. C. and Song, D. Y. and Abolmaesumi, P. and Fichtinger, G.},
doi = {10.1118/1.3416937},
issn = {00942405},
journal = {Medical Physics},
keywords = {Fluoroscopy,Prostate brachytherapy,Registration,Ultrasound},
number = {6},
pages = {2749--2760},
title = {{Registration between ultrasound and fluoroscopy or CT in prostate brachytherapy}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {37},
year = {2010}
}
@article{Fang2009,
abstract = {In this paper, we report new progress in developing the instrument and software platform of a combined X-ray mammography/diffuse optical breast imaging system. Particularly, we focus on system validation using a series of balloon phantom experiments and the optical image analysis of 49 healthy patients. Using the finite-element method for forward modeling and a regularized Gauss-Newton method for parameter reconstruction, we recovered the inclusions inside the phantom and the hemoglobin images of the human breasts. An enhanced coupling coefficient estimation scheme was also incorporated to improve the accuracy and robustness of the reconstructions. The recovered average total hemoglobin concentration (HbT) and oxygen saturation (SO2) from 68 breast measurements are 16.2 $\mu$ and 71{\%}, respectively, where the HbT presents a linear trend with breast density. The low HbT value compared to literature is likely due to the associated mammographic compression. From the spatially co-registered optical/X-ray images, we can identify the chest-wall muscle, fatty tissue, and fibroglandular regions with an average HbT of 20.1 ± 6.1 $\mu$ for fibroglandular tissue, 15.4 ± 5.0 $\mu$ for adipose, and 22.2 ± 7.3 $\mu$ for muscle tissue. The differences between fibroglandular tissue and the corresponding adipose tissue are significant (p≤ 0.0001). At the same time, we recognize that the optical images are influenced, to a certain extent, by mammographical compression. The optical images from a subset of patients show composite features from both tissue structure and pressure distribution. We present mechanical simulations which further confirm this hypothesis. {\textcopyright} 2006 IEEE.},
author = {Fang, Qianqian and Carp, Stefan A. and Selb, Juliette and Boverman, Greg and Zhang, Quan and Kopans, Daniel B. and Moore, Richard H. and Miller, Eric L. and Brooks, Dana H. and Boas, David A.},
doi = {10.1109/TMI.2008.925082},
issn = {02780062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Breast imaging,Multimodality imaging,Tomography},
number = {1},
pages = {30--42},
pmid = {19116186},
title = {{Combined optical imaging and mammography of the healthy breast: Optical contrast derived from breast structure and compression}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-58149166606{\&}doi=10.1109{\%}2FTMI.2008.925082{\&}partnerID=40{\&}md5=0adeb1e35532e1ca200be1b4fda2df76},
volume = {28},
year = {2009}
}
@inproceedings{Fang,
abstract = {We present a software platform for image reconstruction and data analysis for diffuse optical tomography. The structure, algorithm and functionalities of the platform are reported together with the sample results produced by the platform. {\textcopyright} 2007 Optical Society of America.},
author = {Fang, Q. and Carp, S. A. and Selb, J. and Moore, R. and Kopans, D. B. and Miller, E. L. and Brooks, D. H. and Boas, D. A.},
booktitle = {Biomedical Optics, BIOMED 2008},
doi = {10.1364/biomed.2008.bmd24},
title = {{A multi-modality image reconstruction platform for diffuse optical tomography}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884342384{\&}partnerID=40{\&}md5=0a660aea258182c137661a84afe6ec4e},
year = {2008}
}
@inproceedings{Fanga,
abstract = {We imaged 65 patients with a combined optical and tomosynthesis imaging system. The bulk optical properties from 72 healthy breasts and the reconstructed images using a spectrally-constrained algorithm for healthy and tumor breasts are reported. {\textcopyright} 2007 Optical Society of America.},
author = {Fang, Q. and Carp, S. A. and Selb, J. and Moore, R. and Kopans, D. B. and Miller, E. L. and Brooks, D. H. and Boas, D. A.},
booktitle = {Biomedical Optics, BIOMED 2008},
doi = {10.1364/biomed.2008.bsub2},
title = {{Spectrally constrained optical breast imaging with coregistered x-ray tomosynthesis}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84884324617{\&}partnerID=40{\&}md5=4341cad05ad6059b2b7866085d7c9037},
year = {2008}
}
@article{Fehr2015,
abstract = {Noninvasive, radiological image-based detection and stratification of Gleason patterns can impact clinical outcomes, treatment selection, and the determination of disease status at diagnosis without subjecting patients to surgical biopsies. We present machine learning-based automatic classification of prostate cancer aggressiveness by combining apparent diffusion coefficient (ADC) and T2-weighted (T2-w) MRI-based texture features. Our approach achieved reasonably accurate classification of Gleason scores (GS) 6(3 + 3) vs. ≥7 and 7(3 + 4) vs. 7(4 + 3) despite the presence of highly unbalanced samples by using two different sample augmentation techniques followed by feature selection-based classification. Our method distinguished between GS 6(3 + 3) and ≥7 cancers with 93{\%} accuracy for cancers occurring in both peripheral (PZ) and transition (TZ) zones and 92{\%} for cancers occurring in the PZ alone. Our approach distinguished the GS 7(3 + 4) from GS 7(4 + 3) with 92{\%} accuracy for cancers occurring in both the PZ and TZ and with 93{\%} for cancers occurring in the PZ alone. In comparison, a classifier using only the ADC mean achieved a top accuracy of 58{\%} for distinguishing GS 6(3 + 3) vs. GS ≥7 for cancers occurring in PZ and TZ and 63{\%} for cancers occurring in PZ alone. The same classifier achieved an accuracy of 59{\%} for distinguishing GS 7(3 + 4) from GS 7(4 + 3) occurring in the PZ and TZ and 60{\%} for cancers occurring in PZ alone. Separate analysis of the cancers occurring in TZ alone was not performed owing to the limited number of samples. Our results suggest that texture features derived from ADC and T2-w MRI together with sample augmentation can help to obtain reasonably accurate classification of Gleason patterns.},
author = {Fehr, Duc and Veeraraghavan, Harini and Wibmer, Andreas and Gondo, Tatsuo and Matsumoto, Kazuhiro and Vargas, Herbert Alberto and Sala, Evis and Hricak, Hedvig and Deasy, Joseph O.},
doi = {10.1073/pnas.1505935112},
issn = {10916490},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Gleason score classification,Learning from unbalanced data,Multiparametric mri,PCa gleason (3+4) vs. (4+3) cancers,PCa gleason 6 vs. ≥7},
number = {46},
pages = {E6265--E6273},
title = {{Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {112},
year = {2015}
}
@article{FennemaNotestine2016,
abstract = {White matter disease in the brain increases with age and cardiovascular disease, emerging in midlife, and these associations may be influenced by both genetic and environmental factors. We examined the frequency, distribution, and heritability of abnormal white matter and its association with hypertension in 395 middle-aged male twins (61.9 ± 2.6 years) from the Vietnam Era Twin Study of Aging, 67{\%} of whom were hypertensive. A multi-channel segmentation approach estimated abnormal regions within the white matter. Using multivariable regression models, we characterized the frequency distribution of abnormal white matter in midlife and investigated associations with hypertension and Apolipoprotein E-$\epsilon$4 status and the impact of duration and control of hypertension. Then, using the classical twin design, we estimated abnormal white matter heritability and the extent of shared genetic overlap with blood pressure. Abnormal white matter was predominantly located in periventricular and deep parietal and frontal regions; associated with age (t = 1.9, p = 0.05) and hypertension (t = 2.9, p = 0.004), but not Apolipoprotein $\epsilon$4 status; and was greater in those with uncontrolled hypertension relative to controlled (t = 3.0, p = 0.003) and normotensive (t = 4.0, p = 0.0001) groups, suggesting that abnormal white matter may reflect currently active cerebrovascular effects. Abnormal white matter was highly heritable (a2 = 0.81) and shared some genetic influences with systolic blood pressure (rA = 0.26), although there was evidence for distinct genetic contributions and unique environmental influences. Future longitudinal research will shed light on factors impacting white matter disease presentation, progression, and potential recovery.},
author = {Fennema-Notestine, Christine and McEvoy, Linda K. and Notestine, Randy and Panizzon, Matthew S. and Yau, Wai Ying Wendy and Franz, Carol E. and Lyons, Michael J. and Eyler, Lisa T. and Neale, Michael C. and Xian, Hong and McKenzie, Ruth E. and Kremen, William S.},
doi = {10.1016/j.nicl.2016.10.001},
issn = {22131582},
journal = {NeuroImage: Clinical},
keywords = {Blood pressure,Brain,Heritability,Hypertension,MRI,White matter},
pages = {737--745},
title = {{White matter disease in midlife is heritable, related to hypertension, and shares some genetic influence with systolic blood pressure}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {12},
year = {2016}
}
@article{Finnegan2019,
abstract = {Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left-sided breast radiotherapy. Many current studies have relied on estimates of cardiac doses assuming standardised anatomy, with a calculated increase in relative risk of 7.4{\%} per Gy (mean heart dose). To provide individualised estimates for dose, delineation of various cardiac structures on patient images is required. Automatic multi-atlas based segmentation can provide a consistent, robust solution, however there are challenges to this method. We are aiming to develop and validate a cardiac atlas and segmentation framework, with a focus on the limitations and uncertainties in the process. We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation. A dataset consisting of 20 planning computed tomography (CT) images of Australian breast cancer patients with delineations of 17 structures (including whole heart, four chambers, coronary arteries and valves) was manually contoured by three independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manuallyderived contours; measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours. For the whole heart, the resulting segmentation achieved a DSC of 0.944 +0.024, with a MASD of .726 + 1.363 mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the interobserver variability in manual contouring.},
author = {Finnegan, Robert and Dowling, Jason and Koh, Eng Siew and Tang, Simon and Otton, James and Delaney, Geoff and Batumalai, Vikneswary and Luo, Carol and Atluri, Pramukh and Satchithanandha, Athiththa and Thwaites, David and Holloway, Lois},
doi = {10.1088/1361-6560/ab0ea6},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {atlas-based segmentation,computed tomography,heart contouring,image registration,medical image processing,whole heart segmentation},
number = {8},
title = {{Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85064555945{\&}doi=10.1088{\%}2F1361-6560{\%}2Fab0ea6{\&}partnerID=40{\&}md5=65ca7ad7c68a23ee0cb634955c18a67b},
volume = {64},
year = {2019}
}
@article{Frohler2016,
abstract = {We present GEMSe, an interactive tool for exploring and analyzing the parameter space of multi-channel segmentation algorithms. Our targeted user group are domain experts who are not necessarily segmentation specialists. GEMSe allows the exploration of the space of possible parameter combinations for a segmentation framework and its ensemble of results. Users start with sampling the parameter space and computing the corresponding segmentations. A hierarchically clustered image tree provides an overview of variations in the resulting space of label images. Details are provided through exemplary images from the selected cluster and histograms visualizing the parameters and the derived output in the selected cluster. The correlation between parameters and derived output as well as the effect of parameter changes can be explored through interactive filtering and scatter plots. We evaluate the usefulness of GEMSe through expert reviews and case studies based on three different kinds of datasets: A synthetic dataset emulating the combination of 3D X-ray computed tomography with data from K-Edge spectroscopy, a three-channel scan of a rock crystal acquired by a Talbot-Lau grating interferometer X-ray computed tomography device, as well as a hyperspectral image.},
author = {Fr{\"{o}}hler, B. and M{\"{o}}ller, T. and Heinzl, C.},
doi = {10.1111/cgf.12895},
issn = {14678659},
journal = {Computer Graphics Forum},
keywords = {Categories and Subject Descriptors (according to A,I.3.8 [Computer Graphics]: Applications—,I.4.6 [Image Processing and Computer Vision]: Segm},
number = {3},
pages = {191--200},
title = {{GEMSe: Visualization-Guided Exploration of Multi-channel Segmentation Algorithms}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {35},
year = {2016}
}
@article{Fuentes2019,
abstract = {PURPOSE: This pilot study evaluates the feasibility of automated volumetric quantification of hepatocellular carcinoma (HCC) as an imaging biomarker to assess treatment response for sorafenib. METHODS: In this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant retrospective study, a training database of manually labeled background liver, enhancing and nonenhancing tumor tissue was established using pretherapy and first posttherapy multiphasic computed tomography images from a registry of 13 HCC patients. For each patient, Hounsfield density and geometry-based feature images were generated from registered multiphasic computed tomography data sets and used as the input for a random forest-based classifier of enhancing and nonenhancing tumor tissue. Leave-one-out cross-validation of the dice similarity measure was applied to quantify the classifier accuracy. A Cox regression model was used to confirm volume changes as predictors of time to progression (TTP) of target lesions for both manual and automatic methods. RESULTS: When compared with manual labels, an overall classification accuracy of dice similarity coefficient of 0.71 for pretherapy and 0.66 posttherapy enhancing tumor labels and 0.45 for pretherapy and 0.59 for posttherapy nonenhancing tumor labels was observed. Automated methods for quantifying volumetric changes in the enhancing lesion agreed with manual methods and were observed as a significant predictor of TTP. CONCLUSIONS: Automated volumetric analysis was determined to be feasible for monitoring HCC response to treatment. The information extracted using automated volumetrics is likely to reproduce labor-intensive manual data and provide a good predictor for TTP. Further work will extend these studies to additional treatment modalities and larger patient populations.},
author = {Fuentes, David and Ahmed, Kareem and Lin, Jonathan S. and Abdel-Wahab, Reham and Kaseb, Ahmed O. and Hassan, Manal and Szklaruk, Janio and Morshid, Ali and Hazle, John D. and Qayyum, Aliya and Elsayes, Khaled M.},
doi = {10.1097/RCT.0000000000000866},
issn = {15323145},
journal = {Journal of computer assisted tomography},
number = {3},
pages = {499--506},
title = {{Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A Pilot Study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {43},
year = {2019}
}
@article{Fusseis2014,
abstract = {This contribution outlines Synchrotron-based X-ray micro-tomography and its potential use in structural geology and rock mechanics. The paper complements several recent reviews of X-ray microtomography. We summarize the general approach to data acquisition, post-processing as well as analysis and thereby aim to provide an entry point for the interested reader. The paper includes tables listing relevant beamlines, a list of all available imaging techniques, and available free and commercial software packages for data visualization and quantification. We highlight potential applications in a review of relevant literature including time-resolved experiments and digital rock physics. The paper concludes with a report on ongoing developments and upgrades at synchrotron facilities to frame the future possibilities for imaging sub-second processes in centimetre-sized samples. {\textcopyright} 2014 Elsevier Ltd.},
author = {Fusseis, F. and Xiao, X. and Schrank, C. and {De Carlo}, F.},
doi = {10.1016/j.jsg.2014.02.005},
issn = {01918141},
journal = {Journal of Structural Geology},
keywords = {3D petrography,Digital rock physics,Synchrotron,X-ray microtomography},
pages = {1--16},
title = {{A brief guide to synchrotron radiation-based microtomography in (structural) geology and rock mechanics}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {65},
year = {2014}
}
@article{Fuster2013,
abstract = {Subtraction of Ictal SPECT Co-registered to MRI (SISCOM) is an imaging technique used to localize the epileptogenic focus in patients with intractable partial epilepsy. The aim of this study was to determine the accuracy of registration algorithms involved in SISCOM analysis using FocusDET, a new user-friendly application. To this end, Monte Carlo simulation was employed to generate realistic SPECT studies. Simulated sinograms were reconstructed by using the Filtered BackProjection (FBP) algorithm and an Ordered Subsets Expectation Maximization (OSEM) reconstruction method that included compensation for all degradations. Registration errors in SPECT-SPECT and SPECT-MRI registration were evaluated by comparing the theoretical and actual transforms. Patient studies with well-localized epilepsy were also included in the registration assessment. Global registration errors including SPECT-SPECT and SPECT-MRI registration errors were less than 1.2 mm on average, exceeding the voxel size (3.32 mm) of SPECT studies in no case. Although images reconstructed using OSEM led to lower registration errors than images reconstructed with FBP, differences after using OSEM or FBP in reconstruction were less than 0.2 mm on average. This indicates that correction for degradations does not play a major role in the SISCOM process, thereby facilitating the application of the methodology in centers where OSEM is not implemented with correction of all degradations. These findings together with those obtained by clinicians from patients via MRI, interictal and ictal SPECT and video-EEG, show that FocusDET is a robust application for performing SISCOM analysis in clinical practice. {\textcopyright} 2012 Springer Science+Business Media, LLC.},
author = {{Mart{\'{i}} Fuster}, Berta and Esteban, Oscar and Planes, Xavier and Aguiar, Pablo and Crespo, Cristina and Falcon, Carles and Wollny, Gert and {Rub{\'{i}} Sureda}, Sebasti{\`{a}} and Setoain, Xavier and Frangi, Alejandro F. and Ledesma, Maria J. and Santos, Andr{\'{e}}s and Pav{\'{i}}a, Javier and Ros, Dom{\`{e}}nec},
doi = {10.1007/s12021-012-9158-x},
issn = {15392791},
journal = {Neuroinformatics},
keywords = {Epilepsy,Monte Carlo simulation,Reconstruction algorithms,Registration assessment,SISCOM},
number = {1},
pages = {77--89},
title = {{FocusDET, a new toolbox for SISCOM analysis. Evaluation of the registration accuracy using monte carlo simulation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {11},
year = {2013}
}
@inproceedings{Gamilov,
abstract = {1D model is used to simulate blood flow in major vessels of the upper body and head. The 1D part is stated in terms of viscous incompressible fluid flow in the network of elastic tubes. Two different types of junctions are considered: junctions between major vessels and junctions between arteries and veins. Vessel network reconstruction algorithm consists of vessel segmentation, thinning-based obtaining of set of centerlines, and graph reconstruction. Input data is 3D DICOM datasets, obtained with contrast enhanced Computed Tomography (CT) Angiography. Constructed model is used to study the influence of carotid artery stenosis on the direction of blood flow in the circle of Willis.},
author = {Gamilov, Timur and Pryamonosov, Roman and Simakov, Sergey},
booktitle = {ECCOMAS Congress 2016 - Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering},
doi = {10.7712/100016.1793.8690},
isbn = {9786188284401},
keywords = {1D haemodynamics,Carotid artery stenosis,Circle of Willis,Microcirculation,Patient-specific,Vessel segmentation},
pages = {81--89},
title = {{Modeling of patient-specific cases of atherosclerosis in carotid arteries}},
type = {Conference Proceedings},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84995444960{\&}doi=10.7712{\%}2F100016.1793.8690{\&}partnerID=40{\&}md5=47f13a248cbaf7933cea6036588d227b},
volume = {1},
year = {2016}
}
@article{Garpebring2013,
abstract = {Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15{\%} in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15{\%} errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23{\%}, 32{\%}, and 29{\%} for Ktrans, ve, and vp, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty. {\textcopyright} 2012 Wiley Periodicals, Inc.},
author = {Garpebring, Anders and Brynolfsson, Patrik and Yu, Jun and Wirestam, Ronnie and Johansson, Adam and Asklund, Thomas and Karlsson, Mikael},
doi = {10.1002/mrm.24328},
issn = {07403194},
journal = {Magnetic Resonance in Medicine},
keywords = {accuracy,dynamic contrast-enhanced-MRI,precision analysis,uncertainty estimation},
number = {4},
pages = {992--1002},
title = {{Uncertainty estimation in dynamic contrast-enhanced MRI}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {69},
year = {2013}
}
@article{Garpebring2011,
abstract = {Object: Phase-based arterial input functions (AIFs) provide a promising alternative to standard magnitude-based AIFs, for example, because inflow effects are avoided. The usefulness of phase-based AIFs in clinical dynamic contrast-enhanced MRI (DCE-MRI) was investigated, and relevant pitfalls and sources of uncertainty were identified. Materials and methods: AIFs were registered from eight human subjects on, in total, 21 occasions. AIF quality was evaluated by comparing AIFs from right and left internal carotid arteries and by assessing the reliability of blood plasma volume estimates. Results: Phase-based AIFs yielded an average bolus peak of 3.9 mM and a residual concentration of 0.37 mM after 3 min, (0.033 mmol/kg contrast agent injection). The average blood plasma volume was 2.7{\%} when using the AIF peak in the estimation, but was significantly different (p {\textless} 0.0001) and less physiologically reasonable when based on the AIF tail concentration. Motion-induced phase shifts and accumulation of contrast agent in background tissue regions were identified as main sources of uncertainty. Conclusion: Phase-based AIFs are a feasible alternative to magnitude AIFs, but sources of errors exist, making quantification difficult, especially of the AIF tail. Improvement of the technique is feasible and also required for the phase-based AIF approach to reach its full potential. {\textcopyright} 2011 ESMRMB.},
author = {Garpebring, Anders and Wirestam, Ronnie and Yu, Jun and Asklund, Thomas and Karlsson, Mikael},
doi = {10.1007/s10334-011-0257-8},
issn = {09685243},
journal = {Magnetic Resonance Materials in Physics, Biology and Medicine},
keywords = {Arterial input function,Dynamic contrast-enhanced MRI,Phase quantification},
number = {4},
pages = {233--245},
title = {{Phase-based arterial input functions in humans applied to dynamic contrast-enhanced MRI: Potential usefulness and limitations}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {24},
year = {2011}
}
@article{Ge2014,
abstract = {Purpose: Intrafraction deformation limits targeting accuracy in radiotherapy. Studies show tumor deformation of over 10 mm for both single tumor deformation and system deformation (due to differential motion between primary tumors and involved lymph nodes). Such deformation cannot be adapted to with current radiotherapy methods. The objective of this study was to develop and experimentally investigate the ability of a dynamic multi-leaf collimator (DMLC) tracking system to account for tumor deformation. Methods: To compensate for tumor deformation, the DMLC tracking strategy is to warp the planned beam aperture directly to conform to the new tumor shape based on real time tumor deformation input. Two deformable phantoms that correspond to a single tumor and a tumor system were developed. The planar deformations derived from the phantom images in beam's eye view were used to guide the aperture warping. An in-house deformable image registration software was developed to automatically trigger the registration once new target image was acquired and send the computed deformation to the DMLC tracking software. Because the registration speed is not fast enough to implement the experiment in real-time manner, the phantom deformation only proceeded to the next position until registration of the current deformation position was completed. The deformation tracking accuracy was evaluated by a geometric target coverage metric defined as the sum of the area incorrectly outside and inside the ideal aperture. The individual contributions from the deformable registration algorithm and the finite leaf width to the tracking uncertainty were analyzed. Clinical proof-of-principle experiment of deformation tracking using previously acquired MR images of a lung cancer patient was implemented to represent the MRI-Linac environment. Intensity-modulated radiation therapy (IMRT) treatment delivered with enabled deformation tracking was simulated and demonstrated. Results: The first experimental investigation of adapting to tumor deformation has been performed using simple deformable phantoms. For the single tumor deformation, the A u+Ao was reduced over 56{\%} when deformation was larger than 2 mm. Overall, the total improvement was 82{\%}. For the tumor system deformation, the Au+Ao reductions were all above 75{\%} and the total Au+Ao improvement was 86{\%}. Similar coverage improvement was also found in simulating deformation tracking during IMRT delivery. The deformable image registration algorithm was identified as the dominant contributor to the tracking error rather than the finite leaf width. The discrepancy between the warped beam shape and the ideal beam shape due to the deformable registration was observed to be partially compensated during leaf fitting due to the finite leaf width. The clinical proof-of-principle experiment demonstrated the feasibility of intrafraction deformable tracking for clinical scenarios. Conclusions: For the first time, we developed and demonstrated an experimental system that is capable of adapting the MLC aperture to account for tumor deformation. This work provides a potentially widely available management method to effectively account for intrafractional tumor deformation. This proof-of-principle study is the first experimental step toward the development of an image-guided radiotherapy system to treat deforming tumors in real-time. {\textcopyright} 2014 American Association of Physicists in Medicine.},
author = {Ge, Yuanyuan and O'Brien, Ricky T. and Shieh, Chun Chien and Booth, Jeremy T. and Keall, Paul J.},
doi = {10.1118/1.4873682},
issn = {00942405},
journal = {Medical Physics},
keywords = {DMLC tracking,adaptation,tumor deformation},
number = {6},
pages = {10},
title = {{Toward the development of intrafraction tumor deformation tracking using a dynamic multi-leaf collimator}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {41},
year = {2014}
}
@article{Gerst2020,
abstract = {Modern imaging techniques, such as lightsheet fluorescence microscopy (LSFM), allow the capture of whole organs in three spatial dimensions. The analysis of these big volume image data requires a combination of user-friendly and highly efficient tools. We here present MISA++, an image analysis framework that allows easy integration of custom high-performance C++ tools into third-party applications via standardized components for parallelization, data and parameter handling, command line interface, and communication with third-party applications. We demonstrate its capabilities by implementing a plugin for ImageJ that provides a graphical user interface for any application built with our framework, and a high-performance re-implementation of our Python-based algorithm to segment glomeruli in LSFM images of whole murine kidneys.},
author = {Gerst, Ruman and Medyukhina, Anna and Figge, Marc Thilo},
doi = {10.1016/j.softx.2020.100405},
issn = {23527110},
journal = {SoftwareX},
keywords = {Application integration,Big volume image data,Image processing,Light-sheet fluorescence microscopy,Parallelization},
title = {{MISA++: A standardized interface for automated bioimage analysis}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078663806{\&}doi=10.1016{\%}2Fj.softx.2020.100405{\&}partnerID=40{\&}md5=4063263c3ae932dded430bc8192b1704},
volume = {11},
year = {2020}
}
@article{Gillies2017,
abstract = {Purpose: During image-guided prostate biopsy, needles are targeted at tissues that are suspicious of cancer to obtain specimen for histological examination. Unfortunately, patient motion causes targeting errors when using an MR-transrectal ultrasound (TRUS) fusion approach to augment the conventional biopsy procedure. This study aims to develop an automatic motion correction algorithm approaching the frame rate of an ultrasound system to be used in fusion-based prostate biopsy systems. Two modes of operation have been investigated for the clinical implementation of the algorithm: motion compensation using a single user initiated correction performed prior to biopsy, and real-time continuous motion compensation performed automatically as a background process. Methods: Retrospective 2D and 3D TRUS patient images acquired prior to biopsy gun firing were registered using an intensity-based algorithm utilizing normalized cross-correlation and Powell's method for optimization. 2D and 3D images were downsampled and cropped to estimate the optimal amount of image information that would perform registrations quickly and accurately. The optimal search order during optimization was also analyzed to avoid local optima in the search space. Error in the algorithm was computed using target registration errors (TREs) from manually identified homologous fiducials in a clinical patient dataset. The algorithm was evaluated for real-time performance using the two different modes of clinical implementations by way of user initiated and continuous motion compensation methods on a tissue mimicking prostate phantom. Results: After implementation in a TRUS-guided system with an image downsampling factor of 4, the proposed approach resulted in a mean ± std TRE and computation time of 1.6 ± 0.6 mm and 57 ± 20 ms respectively. The user initiated mode performed registrations with in-plane, out-of-plane, and roll motions computation times of 108 ± 38 ms, 60 ± 23 ms, and 89 ± 27 ms, respectively, and corresponding registration errors of 0.4 ± 0.3 mm, 0.2 ± 0.4 mm, and 0.8 ± 0.5. The continuous method performed registration significantly faster (P {\textless} 0.05) than the user initiated method, with observed computation times of 35 ± 8 ms, 43 ± 16 ms, and 27 ± 5 ms for in-plane, out-of-plane, and roll motions, respectively, and corresponding registration errors of 0.2 ± 0.3 mm, 0.7 ± 0.4 mm, and 0.8 ± 1.0. Conclusions: The presented method encourages real-time implementation of motion compensation algorithms in prostate biopsy with clinically acceptable registration errors. Continuous motion compensation demonstrated registration accuracy with submillimeter and subdegree error, while performing {\textless} 50 ms computation times. Image registration technique approaching the frame rate of an ultrasound system offers a key advantage to be smoothly integrated to the clinical workflow. In addition, this technique could be used further for a variety of image-guided interventional procedures to treat and diagnose patients by improving targeting accuracy.},
author = {Gillies, Derek J. and Gardi, Lori and {De Silva}, Tharindu and Zhao, Shuang Ren and Fenster, Aaron},
doi = {10.1002/mp.12441},
issn = {00942405},
journal = {Medical Physics},
keywords = {2D-3D transrectal ultrasound-guided prostate biops,prostate cancer,prostate motion compensation,real-time image registration},
number = {9},
pages = {4708--4723},
title = {{Real-time registration of 3D to 2D ultrasound images for image-guided prostate biopsy:}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {44},
year = {2017}
}
@article{Ginty2019,
abstract = {Purpose:: Transcatheter, beating heart repair techniques for mitral valve regurgitation is a very active area of development. However, it is difficult to both simulate and predict the clinical outcomes of mitral repairs, owing to the complexity of mitral valve geometry and the influence of hemodynamics. We aim to produce a workflow for manufacturing dynamic patient-specific models to simulate the mitral valve for transcatheter repair applications. Methods:: In this paper, we present technology and associated workflow, for using transesophageal echocardiography to generate dynamic physical replicas of patient valves. We validate our workflow using six patient datasets representing patients with unique or particularly challenging pathologies as selected by a cardiologist. The dynamic component of the models and their resultant potential as procedure planning tools is due to a dynamic pulse duplicator that permits the evaluation of the valve models experiencing realistic hemodynamics. Results:: Early results indicate the workflow has excellent anatomical accuracy and the ability to replicate regurgitation pathologies, as shown by colour Doppler ultrasound and anatomical measurements comparing patients and models. Analysis of all measurements successfully resulted in t critical two-tail {\textgreater} t stat and p values {\textgreater} 0.05, thus demonstrating no statistical difference between the patients and models, owing to high fidelity morphological replication. Conclusions:: Due to the combination of a dynamic environment and patient-specific modelling, this workflow demonstrates a promising technology for simulating the complete morphology of mitral valves undergoing transcatheter repairs.},
author = {Ginty, Olivia K. and Moore, John T. and Eskandari, Mehdi and Carnahan, Patrick and Lasso, Andras and Jolley, Matthew A. and Monaghan, Mark and Peters, Terry M.},
doi = {10.1007/s11548-019-01998-y},
issn = {18616429},
journal = {International Journal of Computer Assisted Radiology and Surgery},
keywords = {3D printing,Mitral valve,Mitral valve models,Modelling,Surgical simulation,Transcatheter devices},
number = {7},
pages = {1227--1235},
title = {{Dynamic, patient-specific mitral valve modelling for planning transcatheter repairs}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {14},
year = {2019}
}
@article{Giuly2013,
abstract = {This application note describes a new scalable semi-automatic approach, the Dual Point Decision Process, for segmentation of 3D structures contained in 3D microscopy. The segmentation problem is distributed to many individual workers such that each receives only simple questions regarding whether two points in an image are placed on the same object. A large pool of micro-labor workers available through Amazon's Mechanical Turk system provides the labor in a scalable manner. {\textcopyright} 2013 The Author. Published by Oxford University Press. All rights reserved.},
author = {Giuly, Richard J. and Kim, Keun Young and Ellisman, Mark H.},
doi = {10.1093/bioinformatics/btt154},
issn = {13674803},
journal = {Bioinformatics},
number = {10},
pages = {1359--1360},
title = {{DP2: Distributed 3D image segmentation using micro-labor workforce}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {29},
year = {2013}
}
@article{Godley2009,
abstract = {Available deformable registration methods are often inaccurate over large organ variation encountered, for example, in the rectum and bladder. The authors developed a novel approach to accurately and effectively register large deformations in the prostate region for adaptive radiation therapy. A software tool combining a fast symmetric demons algorithm and the use of masks was developed in C++ based on ITK libraries to register CT images acquired at planning and before treatment fractions. The deformation field determined was subsequently used to deform the delivered dose to match the anatomy of the planning CT. The large deformations involved required that the bladder and rectum volume be masked with uniform intensities of -1000 and 1000 HU, respectively, in both the planning and treatment CTs. The tool was tested for five prostate IGRT patients. The average rectum planning to treatment contour overlap improved from 67{\%} to 93{\%}, the lowest initial overlap is 43{\%}. The average bladder overlap improved from 83{\%} to 98{\%}, with a lowest initial overlap of 60{\%}. Registration regions were set to include a volume receiving 4{\%} of the maximum dose. The average region was 320×210×63, taking approximately 9 min to register on a dual 2.8 GHz Linux system. The prostate and seminal vesicles were correctly placed even though they are not masked. The accumulated doses for multiple fractions with large deformation were computed and verified. The tool developed can effectively supply the previously delivered dose for adaptive planning to correct for interfractional changes. {\textcopyright} 2009 American Association of Physicists in Medicine.},
author = {Godley, Andrew and Ahunbay, Ergun and Peng, Cheng and Li, X. Allen},
doi = {10.1118/1.3095777},
issn = {00942405},
journal = {Medical Physics},
keywords = {Deformable registration,cumulative dose,dose deformation,prostate},
number = {4},
pages = {1433--1441},
title = {{Automated registration of large deformations for adaptive radiation therapy of prostate cancer}},
type = {Journal Article},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-63849158774{\&}doi=10.1118{\%}2F1.3095777{\&}partnerID=40{\&}md5=54c366a7760aa8527e47d27e9d97aaa7},
volume = {36},
year = {2009}
}
@article{Godley2012,
abstract = {Even with daily image guidance based on soft tissue registration, deviations of fractional doses can be quite large due to changes in patient anatomy. It is of interest to ascertain the cumulative effect of these deviations on the total delivered dose. Daily kV CT data acquired using an in-room CT for five prostate cancer patients were analyzed. Each daily CT was deformably registered to the planning CT using an in-house tool. The resulting deformation field was used to map the delivered daily dose onto the planning CT, then summed to obtain the cumulative (total delivered) dose to the patient. The delivered cumulative values of prostate D100 on average were only 2.9{\%} less than their planned values, while the PTV D95 were 3.6{\%} less. The delivered rectum and bladder V70s can be twice what was planned. The less than 3{\%} difference between delivered and planned prostate coverage indicates that the PTV margin of 5 mm was sufficient with the soft-tissue-based kV CT guidance for the cases studied.},
author = {Godley, Andrew and Ahunbay, Ergun and Peng, Cheng and {Allen Li}, X.},
doi = {10.1120/jacmp.v13i3.3859},
issn = {15269914},
journal = {Journal of Applied Clinical Medical Physics},
keywords = {Cumulative dose,Deformable image registration,Prostate radiation therapy,Soft-tissue-based registration},
number = {3},
pages = {98--107},
title = {{Accumulating daily-varied dose distributions of prostate radiation therapy with soft-tissue-based KV CT guidance}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {13},
year = {2012}
}
@article{GomezConde2015,
abstract = {We describe a set of new algorithms and a software tool, StabiTissue, for stabilizing in vivo intravital microscopy images that suffer from soft-tissue background movement. Because these images lack predetermined anchors and are dominated by noise, we use a pixel weighted image alignment together with a correction for nonlinear tissue deformations. We call this correction a poor man[U+05F3]. s diffeomorphic map since it ascertains the nonlinear regions of the image without resorting to a full integral equation method. To determine the quality of the image stabilization, we developed an ensemble sampling method that quantifies the coincidence between image pairs from randomly distributed image regions. We obtain global stabilization alignment through an iterative constrained simulated annealing optimization procedure. To show the accuracy of our algorithm with existing software, we measured the misalignment error rate in datasets taken from two different organs and compared the results to a similar and popular open-source solution. Present open-source stabilization software tools perform poorly because they do not treat the specific needs of the IV-2pM datasets with soft-tissue deformation, speckle noise, full 5D inter- and intra-stack motion error correction, and undefined anchors. In contrast, the results of our tests demonstrate that our method is more immune to noise and provides better performance for datasets' possessing nonlinear tissue deformations. As a practical application of our software, we show how our stabilization improves cell tracking, where the presence of background movement would degrade track information. We also provide a qualitative comparison of our software with other open-source libraries/applications. Our software is freely available at the open source repository http://sourceforge.net/projects/stabitissue/.},
author = {G{\'{o}}mez-Conde, Iv{\'{a}}n and Caetano, Susana S. and Tadokoro, Carlos E. and Olivieri, David N.},
doi = {10.1016/j.compbiomed.2015.07.001},
issn = {18790534},
journal = {Computers in Biology and Medicine},
keywords = {Bioimaging,Biomedical image stabilization,Image registration,In vivo two photon microscopy,Lymphocyte tracking,Soft-tissue deformations},
pages = {246--260},
title = {{Stabilizing 3D in vivo intravital microscopy images with an iteratively refined soft-tissue model for immunology experiments}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {64},
year = {2015}
}
@article{Goncalves2019,
abstract = {Molecular dynamics simulations are performed to investigate the nucleation and growth of cavities in a hydrated Nafion membrane under mechanical deformation. The simulation model used in this study accurately reproduces the experimental values of the elastic modulus of the membrane as a function of water content. The results obtained from triaxial tensile tests reveal a ductile to brittle transition as the water content increases. The nucleation and growth of the cavities have been quantitatively analyzed in terms of the number and size of cavities, illustrating the ductile to brittle transition uncovered by the stress/strain curves. Further local analyses have been carried out to identify the nucleation sites. The analysis of local plasticity indicates that as the water content increases, the membrane accumulates more plastic deformation in the hydrophilic domain than in the hydrophobic domain during the rupture stage of the tensile tests. These results suggest that the water network significantly impacts the nucleation and expansion of cavities induced by mechanical deformation. Furthermore, the local mechanical properties of the Nafion membrane are evaluated. The results show that the mechanical properties are heterogeneous at the nanoscale and that the cavities nucleate in soft regions of the membrane. A statistical analysis of the local water density of nucleation sites indicates that the polymer-water interfaces are more likely to nucleate cavities. The expansion and coalescence of cavities is facilitated by the high molecular reorganization of the water network, which explains the brittle behavior of membranes with high water content.},
author = {Gon{\c{c}}alves, William and Mabuchi, Takuya and Tokumasu, Takashi},
doi = {10.1021/acs.jpcc.9b07101},
issn = {19327455},
journal = {Journal of Physical Chemistry C},
number = {47},
pages = {28958--28968},
title = {{Nucleation and Growth of Cavities in Hydrated Nafion Membranes under Tensile Strain: A Molecular Dynamics Study}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {123},
year = {2019}
}
@article{Grauer2019,
abstract = {Background: There is an increasing interest in local tumor ablative treatment modalities that induce immunogenic cell death and the generation of antitumor immune responses. Methods: We report six recurrent glioblastoma patients who were treated with intracavitary thermotherapy after coating the resection cavity wall with superparamagnetic iron oxide nanoparticles (“NanoPaste” technique). Patients underwent six 1-h hyperthermia sessions in an alternating magnetic field and, if possible, received concurrent fractionated radiotherapy at a dose of 39.6 Gy. Results: There were no major side effects during active treatment. However, after 2–5 months, patients developed increasing clinical symptoms. CT scans showed tumor flare reactions with prominent edema around nanoparticle deposits. Patients were treated with dexamethasone and, if necessary, underwent re-surgery to remove nanoparticles. Histopathology revealed sustained necrosis directly adjacent to aggregated nanoparticles without evidence for tumor activity. Immunohistochemistry showed upregulation of Caspase-3 and heat shock protein 70, prominent infiltration of macrophages with ingested nanoparticles and CD3 + T-cells. Flow cytometric analysis of freshly prepared tumor cell suspensions revealed increased intracellular ratios of IFN-$\gamma$ to IL-4 in CD4 + and CD8 + memory T cells, and activation of tumor-associated myeloid cells and microglia with upregulation of HLA-DR and PD-L1. Two patients had long-lasting treatment responses {\textgreater} 23 months without receiving any further therapy. Conclusion: Intracavitary thermotherapy combined with radiotherapy can induce a prominent inflammatory reaction around the resection cavity which might trigger potent antitumor immune responses possibly leading to long-term stabilization of recurrent GBM patients. These results warrant further investigations in a prospective phase-I trial.},
author = {Grauer, Oliver and Jaber, Mohammed and Hess, Katharina and Weckesser, Matthias and Schwindt, Wolfram and Maring, Stephan and W{\"{o}}lfer, Johannes and Stummer, Walter},
doi = {10.1007/s11060-018-03005-x},
issn = {15737373},
journal = {Journal of Neuro-Oncology},
keywords = {Caspase-3,HSP70,PD-L1,Superparamagnetic iron oxide nanoparticles,Thermotherapy},
number = {1},
pages = {83--94},
pmid = {30506500},
title = {{Combined intracavitary thermotherapy with iron oxide nanoparticles and radiotherapy as local treatment modality in recurrent glioblastoma patients}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {141},
year = {2019}
}
@article{Gribble2019,
abstract = {Mueller polarimetry is a quantitative polarized light imaging modality that is capable of label-free visualization of tissue pathology, does not require extensive sample preparation, and is suitable for wide-field tissue analysis. It holds promise for selected applications in biomedicine, but polarimetry systems are often constrained by limited end-user accessibility and/or long-imaging times. In order to address these needs, we designed a multiscale-polarimetry module that easily couples to a commercially available stereo zoom microscope. This paper describes the module design and provides initial polarimetry imaging results from a murine preclinical breast cancer model and human breast cancer samples. The resultant polarimetry module has variable resolution and field of view, is low-cost, and is simple to switch in or out of a commercial microscope. The module can reduce long imaging times by adopting the main imaging approach used in pathology: scanning at low resolution to identify regions of interest, then at high resolution to inspect the regions in detail. Preliminary results show how the system can aid in region of interest identification for pathology, but also highlight that more work is needed to understand how tissue structures of pathological interest appear in Mueller polarimetry images across varying spatial zoom scales.},
author = {Gribble, Adam and Pinkert, Michael A. and Westreich, Jared and Liu, Yuming and Keikhosravi, Adib and Khorasani, Mohammadali and Nofech-Mozes, Sharon and Eliceiri, Kevin W. and Vitkin, Alex},
doi = {10.1007/s13534-019-00116-w},
issn = {2093985X},
journal = {Biomedical Engineering Letters},
keywords = {Label-free imaging,Module,Mueller matrix polarimetry,Multiscale,Pathology,Stereo zoom microscope},
number = {3},
pages = {339--349},
title = {{A multiscale Mueller polarimetry module for a stereo zoom microscope}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {9},
year = {2019}
}
@article{Griesenauer2017,
abstract = {Tissue stiffness interrogation is fundamental in breast cancer diagnosis and treatment. Furthermore, biomechanical models for predicting breast deformations have been created for several breast cancer applications. Within these applications, constitutive mechanical properties must be defined and the accuracy of this estimation directly impacts the overall performance of the model. In this study, we present an image-derived computational framework to obtain quantitative, patient specific stiffness properties for application in image-guided breast cancer surgery and interventions. The method uses two MR acquisitions of the breast in different supine gravity-loaded configurations to fit mechanical properties to a biomechanical breast model. A reproducibility assessment of the method was performed in a test-retest study using healthy volunteers and was further characterized in simulation. In five human data sets, the within subject coefficient of variation ranged from 10.7{\%} to 27{\%} and the intraclass correlation coefficient ranged from 0.91-0.944 for assessment of fibroglandular and adipose tissue stiffness. In simulation, fibroglandular content and deformation magnitude were shown to have significant effects on the shape and convexity of the objective function defined by image similarity. These observations provide an important step forward in characterizing the use of nonrigid image registration methodologies in conjunction with biomechanical models to estimate tissue stiffness. In addition, the results suggest that stiffness estimation methods using gravity-induced excitation can reliably and feasibly be implemented in breast cancer surgery/intervention workflows.},
author = {Griesenauer, Rebekah H. and Weis, Jared A. and Arlinghaus, Lori R. and Meszoely, Ingrid M. and Miga, Michael I.},
doi = {10.1088/1361-6560/aa700a},
issn = {13616560},
journal = {Physics in Medicine and Biology},
keywords = {Mechanical properties,biomechanical model,breast cancer,elastography,lumpectomy,magnetic resonance imaging,registration},
number = {12},
pages = {4756--4776},
title = {{Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {62},
year = {2017}
}
@article{Gurbani2019,
abstract = {Glioblastoma has poor prognosis with inevitable local recurrence despite aggressive treatment with surgery and chemoradiation. Radiation therapy (RT) is typically guided by contrast-enhanced T1-weighted magnetic resonance imaging (MRI) for defining the high-dose target and T2-weighted fluid-attenuation inversion recovery MRI for defining the moderate-dose target. There is an urgent need for improved imaging methods to better delineate tumors for focal RT. Spectroscopic MRI (sMRI) is a quantitative imaging technique that enables whole-brain analysis of endogenous metabolite levels, such as the ratio of choline-to-N-acetylaspartate. Previous work has shown that choline-to-N-acetylaspartate ratio accurately identifies tissue with high tumor burden beyond what is seen on standard imaging and can predict regions of metabolic abnormality that are at high risk for recurrence. To facilitate efficient clinical implementation of sMRI for RT planning, we developed the Brain Imaging Collaboration Suite (BrICS; https://brainimaging.emory.edu/brics-demo), a cloud platform that integrates sMRI with standard imaging and enables team members from multiple departments and institutions to work together in delineating RT targets. BrICS is being used in a multisite pilot study to assess feasibility and safety of dose-escalated RT based on metabolic abnormalities in patients with glioblastoma (Clinicaltrials.gov NCT03137888). The workflow of analyzing sMRI volumes and preparing RT plans is described. The pipeline achieved rapid turnaround time by enabling team members to perform their delegated tasks independently in BrICS when their clinical schedules allowed. To date, 18 patients have been treated using targets created in BrICS and no severe toxicities have been observed.},
author = {Gurbani, Saumya and Weinberg, Brent and Cooper, Lee and Mellon, Eric and Schreibmann, Eduard and Sheriff, Sulaiman and Maudsley, Andrew and Goryawala, Mohammed and Shu, Hui Kuo and Shim, Hyunsuk},
doi = {10.18383/j.tom.2018.00028},
issn = {2379139X},
journal = {Tomography (Ann Arbor, Mich.)},
keywords = {cloud platform,radiation therapy,spectroscopic MRI},
number = {1},
pages = {184--191},
title = {{The Brain Imaging Collaboration Suite (BrICS): A Cloud Platform for Integrating Whole-Brain Spectroscopic MRI into the Radiation Therapy Planning Workflow}},
type = {Journal Article},
url = {{\%}3CGo to},
volume = {5},
year = {2019}
}
@article{Gustafsson2017,
abstract = {Dosimetric errors in a magnetic resonance imaging (MRI) only radiotherapy workflow may be caused by system specific geometric distortion from MRI. The aim of this study was to evaluate the impact on planned dose distribution and delineated structures for prostate patients, originating from this distortion. A method was developed, in which computer tomography (CT) images were distorted using the MRI distortion field. The displacement map for an optimized MRI treatment planning sequence was measured using a dedicated phantom in a 3 T MRI system. To simulate the distortion aspects of a synthetic CT (electron density derived from MR images), the displacement map was applied to CT images, referred to as distorted CT images. A volumetric modulated arc prostate treatment plan was applied to the original CT and the distorted CT, creating a reference and a distorted CT dose distribution. By applying the inverse of the displacement map to the distorted CT dose distribution, a dose distribution in the same geometry as

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