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Correction: Characterizing the neurological phenotype of the hyperinsulinism hyperammonemia syndrome

Orphanet journal of rare diseases 2022年第1期17卷 315页

作者： Elizabeth Rosenfeld Ravi Prakash Reddy Nanga Alfredo Lucas Andrew Y Revell Allison Thomas Nina H Thomas David R Roalf Russell T Shinohara Ravinder Reddy Kathryn A Davis Diva D De León Division of Endocrinology and Diabetes Children's Hospital of Philadelphia 3500 Civic Center Boulevard Philadelphia PA 19140 USA. rosenfelde@chop.edu. Congenital Hyperinsulinism Center Children's Hospital of Philadelphia Philadelphia PA USA. rosenfelde@chop.edu. Department of Pediatrics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. rosenfelde@chop.edu. Center for Advanced Metabolic Imaging in Precision Medicine Hospital of the University of Pennsylvania Philadelphia PA USA. Penn Center for Neuroengineering and Therapeutics University of Pennsylvania Philadelphia PA USA. Behavioral Neuroscience Core Center for Human Phenomic Science Children's Hospital of Philadelphia Philadelphia PA USA. Department of Child and Adolescent Psychiatry and Behavioral Sciences Children's Hospital of Philadelphia Philadelphia PA USA. Department of Psychiatry University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Penn Statistics in Imaging and Visualization Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Center for Biomedical Image Computing and Analytics Department of Radiology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Department of Neurology University of Pennsylvania Perelman School of Medicine Philadelphia PA USA. Division of Endocrinology and Diabetes Children's Hospital of Philadelphia 3500 Civic Center Boulevard Philadelphia PA 19140 USA. Congenital Hyperinsulinism Center Children's Hospital of Philadelphia Philadelphia PA USA. Department of Pediatrics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA.

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Bayesian spatial binary regression for label fusion in structural neuroimaging

arXiv

引用

arXiv 2017年

作者： Brown, D. Andrew McMahan, Christopher S. Shinohara, Russell T. Linn, Kristin A. School Of Mathematical And Statistical Sciences Clemson University ClemsonSC29634 United States Penn Statistics In Imaging And Visualization Center Department Of Biostatistics Epidemiology And Informatics Center For Biomedical Image Computing And Analytics Department Of Radiology Perelman School Of Medicine University Of Pennsylvania PhiladelphiaPA19104 United States

Alzheimer's disease is a neurodegenerative condition that accelerates cognitive decline relative to normal aging. It is of critical scientific importance to gain a better understanding of early disease mechanisms in the brain to facilitate effective, targeted therapies. The volume of the hippocampus is often used in diagnosis and monitoring of the disease. Measuring this volume via neuroimaging is difficult since each hippocampus must either be manually identified or automatically delineated, a task referred to as segmentation. Automatic hippocampal segmentation often involves mapping a previously manually segmented image to a new brain image and propagating the labels to obtain an estimate of where each hippocampus is located in the new image. A more recent approach to this problem is to propagate labels from multiple manually segmented atlases and combine the results using a process known as label fusion. To date, most label fusion algorithms employ voting procedures with voting weights assigned directly or estimated via optimization. We propose using a fully Bayesian spatial regression model for label fusion that facilitates direct incorporation of covariate information while making accessible the entire posterior distribution. Our results suggest that incorporating tissue classification (e.g, gray matter) into the label fusion procedure can greatly improve segmentation when relatively homogeneous, healthy brains are used as atlases for diseased brains. The fully Bayesian approach also produces meaningful uncertainty measures about hippocampal volumes, information which can be leveraged to detect significant, scientifically meaningful differences between healthy and diseased populations, improving the potential for early detection and tracking of the disease. © 2017, CC BY.

关键词： Neurodegenerative diseases

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***: Dissemination platform for deep learning models

arXiv

引用

arXiv 2019年

作者： Hosny, Ahmed Schwier, Michael Berger, Christoph Örnek, Evin P. Turan, Mehmet Tran, Phi V. Weninger, Leon Isensee, Fabian Maier-Hein, Klaus H. McKinley, Richard Lu, Michael T. Hoffmann, Udo Menze, Bjoern Bakas, Spyridon Fedorov, Andriy Aerts, Hugo J.W.L. Program Brigham and Women's Hospital Harvard Medical School BostonMA United States Department of Radiation Oncology Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Department of Radiology Brigham and Women's Hospital Dana-Farber Cancer Institute Harvard Medical School BostonMA United States Harvard Medical School BostonMA United States Institute for Advanced Study Department of Informatics Technical University of Munich Munich Germany Max Planck Institute for Intelligent Systems Stuttgart Germany Booz Allen Hamilton McLeanVA United States Institute of Imaging & Computer Vision RWTH Aachen University Aachen Germany Heidelberg Germany Support Centre for Advanced Neuroimaging University Institute of Diagnostic Interventional Neuroradiology Inselspital Bern University Hospital Bern Switzerland Cardiovascular Imaging Research Center Massachusetts General Hospital Harvard Medical School Boston United States Cardiac MR PET CT Program Department of Radiology Massachusetts General Hospital BostonMA United States Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Radiology & Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Radiology and Nuclear Medicine GROW CARIM Maastricht University Maastricht Netherlands

Recent advances in artificial intelligence research have led to a profusion of studies that apply deep learning to problems in image analysis and natural language processing among others. Additionally, the availability of open-source computational frameworks has lowered the barriers to implementing state-of-the-art methods across multiple domains. Albeit leading to major performance breakthroughs in some tasks, effective dissemination of deep learning algorithms remains challenging, inhibiting reproducibility and benchmarking studies, impeding further validation, and ultimately hindering their effectiveness in the cumulative scientific progress. In developing a platform for sharing research outputs, we present *** (***), a community-driven container-based software engine and platform for the structured dissemination of deep learning models. For contributors, the engine controls data flow throughout the inference cycle, while the contributor-facing standard template exposes model-specific functions including inference, as well as pre- and post-processing. Python and RESTful Application programming interfaces (APIs) enable users to interact with models hosted on *** and allows both researchers and developers to utilize models out-of-the-box. *** is domain-, data-, and framework-agnostic, catering to different workflows and contributors' preferences. Copyright © 2019, The Authors. All rights reserved.

关键词： Containers

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Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge

arXiv

引用

arXiv 2018年

作者： Veta, Mitko Heng, Yujing J. Stathonikos, Nikolas Bejnordi, Babak Ehteshami Beca, Francisco Wollmann, Thomas Rohr, Karl Shah, Manan A. Wang, Dayong Rousson, Mikael Hedlund, Martin Tellez, David Ciompi, Francesco Zerhouni, Erwan Lanyi, David Viana, Matheus Kovalev, Vassili Liauchuk, Vitali Phoulady, Hady Ahmady Qaiser, Talha Graham, Simon Rajpoot, Nasir Sjöblom, Erik Molin, Jesper Paeng, Kyunghyun Hwang, Sangheum Park, Sunggyun Jia, Zhipeng Chang, Eric I-Chao Xu, Yan Beck, Andrew H. Van Diest, Paul J. Pluim, Josien P.W. Medical Image Analysis Group Eindhoven University of Technology Eindhoven Netherlands Department of Pathology Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology University Medical Center Utrecht Utrecht Netherlands Diagnostic Image Analysis Group Radboud University Medical Center Nijmegen Netherlands Department of Pathology Stanford University School of Medicine Biomedical Computer Vision Group University of Heidelberg BIOQUANT IPMB DKFZ Heidelberg Germany Harker School San Jose United States ContextVision AB Linköping Sweden Foundations of Cognitive Computing IBM Research Zürich Rüschlikon Switzerland Visual Analytics and Insights IBM Research Brazil Saõ Paulo Brazil Biomedical Image Analysis Department United Institute of Informatics Minsk Belarus Department of Computer Science and Engineering University of South Florida TampaFL United States Department of Computer Science University of Warwick Warwick United Kingdom Research Sectra Linköping Sweden Lunit Inc. Seoul Korea Republic of Institute for Interdisciplinary Information Sciences Tsinghua University Beijing China Microsoft Research Beijing China Biology and Medicine Engineering Beihang University Beijing China IBM Research Brazil

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. However, in a realworld scenario, automatic mitosis detection should be performed in whole-slide images (WSIs) and an automatic method should be able to produce a tumor proliferation score given a WSI as input. To address this, we organized the TUmor Proliferation Assessment Challenge 2016 (TUPAC16) on prediction of tumor proliferation scores from WSIs. The challenge dataset consisted of 500 training and 321 testing breast cancer histopathology WSIs. In order to ensure fair and independent evaluation, only the ground truth for the training dataset was provided to the challenge participants. The first task of the challenge was to predict mitotic scores, i.e., to reproduce the manual method of assessing tumor proliferation by a pathologist. The second task was to predict the gene expression based PAM50 proliferation scores from the WSI. The best performing automatic method for the first task achieved a quadratic-weighted Cohen's kappa score of κ = 0.567, 95% CI [0.464, 0.671] between the predicted scores and the ground truth. For the second task, the predictions of the top method had a Spearman's correlation coefficient of r = 0.617, 95% CI [0.581 0.651] with the ground truth. This was the first comparison study that investigated tumor proliferation assessment from WSIs. The achieved results are promising given the difficulty of the tasks and weakly-labeled nature of the ground truth. However, further research is needed to improve the practical utility of image analysis methods for this task. Copyright © 2018, The Authors. All rights reserved.

关键词： Diseases

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Publisher Correction: Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo

Nature Mental Health

引用

Nature Mental Health 2024年 1120页

作者： Cynthia H. Y. Fu Danilo Arnone Roland Zahn Allan H. Young Mathilde Antoniades Guray Erus Jose A. Garcia Yong Fan Haochang Shou Ashish Singh Christos Davatzikos Stephen R. Arnott Stephen C. Strother Taolin Chen Kun Qin Qiyong Gong Ki Sueng Choi Helen S. Mayberg Cherise Chin Fatt Madhukar H. Trivedi Benicio N. Frey Vibe G. Frokjaer Melanie Ganz Gitte M. Knudsen Beata R. Godlewska Catherine J. Harmer Stefanie Hassel Keith Ho Susan Rotzinger Sidney H. Kennedy Andrew M. McIntosh Aleks Stolicyn Heather C. Whalley Matthew D. Sacchet Jonathan Savitz Teresa A. Victor Martin P. Paulus Irina Strigo Duygu Tosun Dongtao Wei Jiang Qiu Toby Wise Ian M. Anderson J. F. William Deakin Rebecca Elliott W. Edward Craighead Boadie W. Dunlop Ian H. Gotlib Chao-Gan Yan School of Psychology University of East London London UK Centre for Affective Disorders Department of Psychological Medicine Institute of Psychiatry Psychology and Neuroscience King’s College London London UK South London and Maudsley NHS Foundation Trust Bethlem Royal Hospital London UK Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center Department of Biostatistics Epidemiology and Informatics University of Pennsylvania Philadelphia PA USA Rotman Research Institute Baycrest Centre Toronto Ontario Canada Department of Medical Biophysics University of Toronto Toronto Ontario Canada Huaxi MR Research Center Department of Radiology West China Hospital Sichuan University Chengdu China Research Unit of Psychoradiology Chinese Academy of Medical Sciences Chengdu China Department of Radiology Taihe Hospital Hubei University of Medicine Shiyan China Nash Family Center for Advanced Circuit Therapeutics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Center for Depression Research and Clinical Care University of Texas Southwestern Medical Center Dallas TX USA Department of Psychiatry and Behavioural Neurosciences McMaster University Hamilton Ontario Canada Mood Disorders Treatment and Research Centre and Women’s Health Concerns Clinic St Joseph’s Healthcare Hamilton Hamilton Ontario Canada Neurobiology Research Unit University Hospital Rigshospitalet Copenhagen Denmark Department of Clinical Medicine Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Department of Psychiatry Psychiatric Centre Copenhagen Copenhagen Denmark Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Psychiatry University of Oxford Oxford UK Oxford Health NHS Foundation Trust Warneford Hospital Oxford UK Mathison Centre for Me

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The Alzheimer's Disease Prediction of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

arXiv

引用

arXiv 2020年

作者： Marinescu, Razvan V. Oxtoby, Neil P. Young, Alexandra L. Bron, Esther E. Toga, Arthur W. Weiner, Michael W. Barkhof, Frederik Fox, Nick C. Eshaghi, Arman Ansart, Manon Durrleman, Stanley Lu, Pascal Iddi, Samuel Li, Dan Thompson, Wesley K. Donohue, Michael C. Nahon, Aviv Levy, Yarden Halbersberg, Dan Cohen, Mariya Liao, Huiling Li, Tengfei Yu, Kaixian Zhu, Hongtu Tamez-Peña, José G. Ismail, Aya Wood, Timothy Bravo, Hector Corrada Nguyen, Minh Sun, Nanbo Feng, Jiashi Thomas Yeo, B.T. Chen, Gang Qi, Ke Chen, Shiyang Qiu, Deqiang Buciuman, Ionut Kelner, Alex Pop, Raluca Rimocea, Denisa Ghazi, Mostafa M. Nielsen, Mads Ourselin, Sebastien Sørensen, Lauge Venkatraghavan, Vikram Liu, Keli Rabe, Christina Manser, Paul Hill, Steven M. Howlett, James Huang, Zhiyue Kiddle, Steven Mukherjee, Sach Rouanet, Anaïs Taschler, Bernd Tom, Brian D.M. White, Simon R. Faux, Noel Sedai, Suman de Velasco Oriol, Javier Clemente, Edgar E.V. Estrada, Karol Aksman, Leon Altmann, Andre Stonnington, Cynthia M. Wang, Yalin Wu, Jianfeng Devadas, Vivek Fourrier, Clementine Raket, Lars Lau Sotiras, Aristeidis Erus, Guray Doshi, Jimit Davatzikos, Christos Vogel, Jacob Doyle, Andrew Tam, Angela Diaz-Papkovich, Alex Jammeh, Emmanuel Koval, Igor Moore, Paul Lyons, Terry J. Gallacher, John Tohka, Jussi Ciszek, Robert Jedynak, Bruno Pandya, Kruti Bilgel, Murat Engels, William Cole, Joseph Golland, Polina Klein, Stefan Alexander, Daniel C. Centre for Medical Image Computing University College London United Kingdom Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Biomedical Imaging Group Rotterdam Department of Radiology and Nuclear Medicine Erasmus MC Netherlands Laboratory of NeuroImaging University of Southern California United States Center for Imaging of Neurodegenerative Diseases University of California San Francisco United States Department of Radiology and Nuclear Medicine VU Medical Centre Netherlands Dementia Research Centre The UK Dementia Research Institute UCL Queen Square Institute of Neurology United Kingdom Institut du Cerveau et de la Moelle épinière Paris France Alzheimer's Therapeutic Research Institute University of Southern California United States Department of Statistics and Actuarial Science University of Ghana Ghana Department of Family Medicine and Public Health University of California San Diego United States Ben Gurion University of the Negev Beersheba Israel The University of Texas Health Science Center at Houston Houston United States Instituto Tecnológico y de Estudios Superiores de Monterrey Monterrey Mexico University of Maryland College Park United States National University of Singapore Singapore Singapore Medical College of Wisconsin Milwaukee United States Emory University Atlanta United States Georgia Institute of Technology Atlanta United States Vasile Lucaciu National College Baia Mare Romania Biomediq A/S Denmark Cerebriu A/S Denmark University of Copenhagen Denmark School of Biomedical Engineering and Imaging Sciences King's College London United Kingdom Genentech United States MRC Biostatistics Unit University of Cambridge United Kingdom IBM Research Australia Melbourne Australia Brandeis University Waltham United States Mayo Clinic ScottsdaleAZ United States H. Lundbeck A/S Denmark Clinical Memory Research Unit Department of Clinical Sciences Malmo Lund University Lund Sweden Center

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADASCog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 perce

关键词： Forecasting

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Author Correction: Federated learning enables big data for rare cancer boundary detection

引用

Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Brain extraction from normal and pathological images: A joint PCA/image-reconstruction approach

arXiv

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arXiv 2017年

作者： Han, Xu Kwitt, Roland Aylward, Stephen Bakas, Spyridon Menze, Bjoern Asturias, Alexander Vespa, Paul Van Horn, John Niethammer, Marc Department of Computer Science University of North Carolina at Chapel Hill United States Department of Computer Science University of Salzburg Austria Kitware Inc. United States Center for Biomedical Image Computing and Analytics Perelman School of Medicine University of Pennsylvania United States Department of Computer Science Technical University of Munich Germany Institute of Neuroimaging and Informatics University of Southern California United States David Geffen School of Medicine UCLA Medical Center United States

Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction;consequently, the brain may not be correctly extracted. This paper proposes a brain extraction approach which can explicitly account for pathologies by jointly modeling normal tissue appearance and pathologies. Specifically, our model uses a three-part image decomposition: (1) normal tissue appearance is captured by principal component analysis (PCA), (2) pathologies are captured via a total variation term, and (3) the skull and surrounding tissue is captured by a sparsity term. Due to its convexity, the resulting decomposition model allows for efficient optimization. Decomposition and image registration steps are alternated to allow statistical modeling of normal tissue appearance in a fixed atlas coordinate system. As a beneficial side effect, the decomposition model allows for the identification of potentially pathological areas and the reconstruction of a quasi-normal image in atlas space. We demonstrate the effectiveness of our approach on four datasets: The publicly available IBSR and LPBA40 datasets which show normal image appearance, the BRATS dataset containing images with brain tumors, and a dataset containing clinical TBI images. We compare the performance with other popular brain extraction models: ROBEX, BEaST, MASS, BET, BSE and a recently proposed deep learning approach. Our model performs better than these competing approaches on all four datasets. Specifically, our model achieves the best median (97.11) and mean

关键词： Principal component analysis

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

引用

arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Simultaneous gene selection and cancer classification using chemical reaction optimization

Simultaneous gene selection and cancer classification using ...

引用

World Congress on Engineering, WCE 2014

作者： Doshi, Jitesh Chindhe, Mahesh Kharche, Yogesh Ghosh, Shameek Valadi, Jayaraman Bionfonrmatics Centre University of Pune Pune India Bioengineering Dept. University of Illinois at Chicago Chicago IL 60607 United States Dept. of Computer Science University of Pune Pune Maharashtra 411007 India Evolutionary Computing and Image Processing Group Centre for Development of Advanced Computing University of Pune Pune Maharashtra 411007 India Advanced Analytics Institute University of Technology Sydney Broadway NSW 2007 Australia Center for Informatics Shiv Nadar University Chithera Dadri Gautam Budh Nagar Uttar Pradesh 203207 India

ISBN: (纸本)9789881925275

Microarray gene expression datasets comprise of a large number of genes in contrast to a small number of samples, thus having a high dimension of variables. Analysis of microarray data can lead us to many useful conclusions. In many microarray data analyses, selecting a small subset of genes which are of significance for a particular type of disease is an important issue but selection of such genes become difficult due to many irrelevant genes and noisy genes. The process of gene selection helps to extract the most informative genes, which consequently aid to build a robust prediction model using those genes. In this study, we employ a hybrid Chemical Reaction Optimization (CRO) based filter-wrapper methodology, which uses an information gain gene ranking heuristic to simultaneously extract informative gene subsets and build robust cancer classification models. The performance of the proposed method was tested on three benchmark gene expression datasets obtained from the Kent Ridge Biomedical datasets collection and the LIBSVM data repository. CRO results demonstrate its capability to select relevant genes with high confidence in comparison to the results reported earlier.

关键词： Classification (of information)

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