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IEEE International Symposium on biomedical Imaging

作者： Mohammad Bajammal Burak Yoldemir Rafeef Abugharbieh Biomedical Signal and Image Computing Lab The University of British Columbia Canada

ISBN: (纸本)9781467393300

Multimodal brain image analysis is gaining popularity as exemplified in the recent surge of techniques dedicated to fusing structural and functional connectivity information. The performance of such endeavors relies on the metrics used to quantify connection strength. Compared to functional connectivity (FC) metrics, structural connectivity (SC) metrics received less scrutiny by the neuroimaging community despite being widely utilized in the literature. In this paper, we analyze the performance of commonly used SC metrics. Specifically, we analyze the relationship between SC and FC during resting-state and different tasks with the assumption of an inherent dependence between brain structure and function. Among the tested metrics, we show that parcel volume-normalized fiber count correlates best with FC, and that total fiber length has the least bias in favor of shorter distances between brain regions. We also show relatively consistent SC-FC correlation across tasks, supporting the notion that SC constitutes the backbone of brain connectivity facilitating a diverse repertoire of functional connectivity patterns.

关键词： Measurement Correlation Optical fiber networks Optical fiber testing Brain Magnetic resonance imaging

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on biomedical Imaging (ISBI) 2017 and the International Conferences on Medical image computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Coupled stable overlapping replicator dynamics for multimodal brain subnetwork identification 24th

Coupled stable overlapping replicator dynamics for multimoda...

引用

24th International Conference on Information Processing in Medical Imaging, IPMI 2015

作者： Yoldemir, Burak Ng, Bernard Abugharbieh, Rafeef Biomedical Signal and Image Computing Lab The University of British Columbia VancouverBC Canada Parietal Team INRIA Saclay Saclay France Functional Imaging in Neuropsychiatric Disorders Lab Stanford University StanfordCA United States

Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (fMRI) and diffusion MRI (dMRI) data, allows for subnetwork overlaps, has an intrinsic criterion for setting the number of subnetworks, and provides statistical control on false node inclusion in the identified subnetworks via the incorporation of stability selection. We thus refer to our technique as coupled stable overlapping replicator dynamics (CSORD). On synthetic data, we demonstrate that CSORD achieves significantly higher subnetwork identification accuracy than state-ofthe- art techniques. On real data from the Human Connectome Project (HCP), we show that CSORD attains improved test-retest reliability on multiple network measures and superior task classification accuracy. © Springer International Publishing Switzerland 2015.

关键词： Brain

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A 3D dynamic biomechanical swallowing model for training and diagnosis of dysphagia

A 3D dynamic biomechanical swallowing model for training and...

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IEEE International Symposium on biomedical Imaging

作者： Moshiur Rahman Farazi Bonnie Martin-Harris Negar M. Harandi Sidney Fels Rafeef Abugharbieh Biomedical Signal and Image Computing Lab Human Communication Technologies Lab Electrical and Computer Engineering University of British Columbia Canada Department of Otolaryngology-Head and Neck Surgery Medical University of South Carolina USA

ISBN: (纸本)9781467393300

We present a three dimensional (3D) biomechanical swallowing model of the oral, pharyngeal and laryngeal (OPAL) muscles and structures. Such modeling may aid in predicting functional outcomes in swallowing disorder (i.e. dysphagia) treatment and could significantly reduce therapy time. Our physics-based model captures the OPAL anatomical geometries and kinematics from 2D animations constructed from video-fluoroscopic (VF) evaluations of real patient swallowing events using the Modified Barium Swallow Impairment Profile (MBSImP™©) protocol. We investigate the upper airway dynamics with these clinically accurate kinematics and geometries. We use smoothed particle hydrodynamics (SPH) modeling of water-like and nectar-like fluid boluses, simulated within an airway-skin mesh that encompasses our modeled 3D structures and follows the model's dynamics. We demonstrate that our model can simulate a bolus in a manner consistent with clinical data, and can robustly handle fluid with different viscosity incorporating a wide range of moving boundary conditions.

关键词： Biological system modeling Atmospheric modeling Solid modeling Animation Biomechanics Fluids Three-dimensional displays

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

引用

Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Automatic assessment of developmental dysplasia of the hip

Automatic assessment of developmental dysplasia of the hip

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IEEE International Symposium on biomedical Imaging

作者： Niamul Quader Antony Hodgson Kishore Mulpuri Thomas Savage Rafeef Abugharbieh Department of Electrical and Computer Engineering Biomedical Signal and Image Computing Lab Department of Mechanical Engineering University of British Columbia Canada Pediatric Orthopedics Pediatric Radiology British Columbia Children's Hospital Canada

Developmental dysplasia of the hip (DDH) refers to a spectrum of hip joint abnormalities that may lead to significant adverse consequences if not detected and treated early in infancy. Clinical exam with selective ultrasound (US) imaging is the current standard used for early detection and diagnosis of DDH in infants up to 6 months of age. However, current US systems require specialized training and have considerable between-center variability, which raises concerns of missed early diagnoses and subsequent complications from late treatment. We propose a novel, automatic, and near real-time image analysis approach that extracts bone contours in US images of the neonatal hip and calculates image based dysplasia metrics (alpha and beta angles). We present quantitative validation on 54 US scans of femoral head flexion collected from 10 infants under age of 3 months.

关键词： Bones Hip Measurement Ultrasonic imaging Pediatrics Feature extraction Band-pass filters

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Effects of tractography approach on consistency between anatomical and functional connectivity estimates

Effects of tractography approach on consistency between anat...

引用

IEEE International Symposium on biomedical Imaging

作者： Burak Yoldemir Bernard Ng Rafeef Abugharbieh Biomedical Signal and Image Computing Lab The University of British Columbia Canada Parietal Team INRIA France Functional Imaging in Neuropsychiatric Disorders Lab Stanford University USA

Diffusion magnetic resonance imaging (dMRI) and resting state functional MRI (RS-fMRI) provide two complementary views of brain circuitry. dMRI facilitates the estimation of anatomical connectivity (AC) through fiber tractography, while RS-fMRI enables the estimation of functional connectivity (FC) based on temporal signal correlations between different brain areas. Recently, there is a methodological push in developing techniques to integrate dMRI and RS-fMRI for multimodal connectivity estimation, success of which highly depends on the consistency between the AC and FC estimates. Using the Human Connectome Project (HCP) data, we show increased AC-FC consistency with streamline tractography on orientation distribution functions (ODFs) compared to using conventional diffusion tensors. We also demonstrate a further, though smaller, improvement when global tractography on ODFs is deployed. Our results suggest that while accurate ODF estimation is important, more attention should be focused on improving tractography methods, which we believe could be highly beneficial.

关键词： Correlation Tensile stress Estimation image reconstruction Magnetic resonance imaging Brain modeling

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Biomechanical kidney model for predicting tumor displacement in the presence of external pressure load

Biomechanical kidney model for predicting tumor displacement...

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IEEE International Symposium on biomedical Imaging

作者： Ivan Figueroa-Garcia Jean-Marc Peyrat Ghassan Hamarneh Rafeef Abugharbieh Biomedical Signal and Image Computing Lab University of British Columbia Vancouver BC Canada Qatar Science & Technology Park Qatar Robotic Surgery Centre Doha Qatar Medical Image Analysis Lab Simon Fraser University Vancouver BC Canada

Robot-assisted partial nephrectomy (RAPN) is a minimally invasive surgery for the treatment of renal cell carcinoma that consists of removing the portion of the kidney that contains the tumor. To plan the resection, surgeons rely on preoperative scans of the patient. However, at surgery time, the shape of abdominal organs differ from these images due to factors such as patient position, insufflation and manipulation with surgical instruments. In this work, we focus on the simulation of kidney deformation due to an external pressure load, e.g. during insufflation, to provide a better estimation of the tumor mass position that is particularly important to plan resection with proper margins. The CT scans of ex vivo lamb kidneys with artificial tumors and fiducials are acquired in absence of external pressure load. From these images, 3D tetra-hedral meshes of kidney parenchyma and tumor, as well as a triangular mesh of the capsule, are extracted and then used along with a soft tissue biomechanical model to simulate deformations under additional external pressure load. A second CT scan of the same kidneys under real pressure load are acquired as a reference to evaluate the advantage of simulating deformations over using the first CT scan without external pressure load. Results show that the biomechanical simulation improves by 29% the tumor localization.

关键词： Tumors Kidney Surgery Biological system modeling Load modeling Biomechanics Computed tomography

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Towards multi-modal image-guided tumour identification in robot-assisted partial nephrectomy

Towards multi-modal image-guided tumour identification in ro...

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Middle East Conference on biomedical Engineering (MECBME)

作者： Ghassan Hamarneh Alborz Amir-Khalili Masoud S. Nosrati Ivan Figueroa Jeremy Kawahara Osama Al-Alao Jean-Marc Peyrat Julien Abi-Nahed Abdulla Al-Ansari Rafeef Abugharbieh Medical Image Analysis Lab Simon Fraser University Burnaby Canada Biomedical Signal and Image Computing Lab University of British Columbia Vancouver Canada Urology Department Hamad Medical Corporation Doha Qatar Qatar Science & Technology Park Qatar Robotic Surgery Centre Doha Qatar

Tumour identification is a critical step in robot-assisted partial nephrectomy (RAPN) during which the surgeon determines the tumour localization and resection margins. To help the surgeon in achieving this step, our research work aims at leveraging both pre- and intra-operative imaging modalities (CT, MRI, laparoscopic US, stereo endoscopic video) to provide an augmented reality view of kidney-tumour boundaries with uncertainty-encoded information. We present herein the progress of this research work including segmentation of preoperative scans, biomechanical simulation of deformations, stereo surface reconstruction from stereo endoscopic camera, pre-operative to intra-operative data registration, and augmented reality visualization.

关键词： Tumors Surgery Kidney Computed tomography Surface reconstruction Stereo image processing Biological system modeling

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Fiber connectivity integrated brain activation detection

Fiber connectivity integrated brain activation detection

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23rd International Conference on Information Processing in Medical Imaging, IPMI 2013

作者： Yoldemir, Burak Ng, Bernard Woodward, Todd S. Abugharbieh, Rafeef Biomedical Signal and Image Computing Lab. University of British Columbia Canada Parietal Team INRIA Saclay France Department of Psychiatry University of British Columbia Canada

ISBN: (纸本)9783642388675

Inference of brain activation through the analysis of functional magnetic resonance imaging (fMRI) data is seriously confounded by the high level of noise in the observations. To mitigate the effects of noise, we propose incorporating anatomical connectivity into brain activation detection as motivated by how the functional integration of distinct brain areas is facilitated via neural fiber pathways. In this work, we formulate activation detection as a probabilistic graph-based segmentation problem with fiber networks estimated from diffusion MRI (dMRI) data serving as a prior. Our approach is reinforced with a data-driven scheme for refining the connectivity prior to reflect the fact that not all fibers are necessarily deployed during a given cognitive task as well as to account for false fiber tracts arising from limitations of dMRI tractography. Validating on real clinical data collected from 7 schizophrenia patients and 13 matched healthy controls, we show that incorporating anatomical connectivity significantly increases sensitivity in detecting task activation in controls compared to existing univariate techniques. Further, we illustrate how our model enables the detection of significant group activation differences between controls and patients that are missed with standard methods. © 2013 Springer-Verlag.

关键词： Magnetic resonance imaging

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