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A large annotated medical image dataset for the development and evaluation of segmentation algorithms
Amber L. Simpson,Michela Antonelli,Spyridon Bakas,Michel Bilello,Keyvan Farahani,Bram van Ginneken,Annette Kopp-Schneider,Bennett A. Landman,Geert Litjens,Bjoern H. Menze,Olaf Ronneberger,Ronald M. Summers,Patrick Bilic,Patrick Ferdinand Christ,Richard K. G. Do,Marc J. Gollub,Jennifer Golia-Pernicka,Stephan Heckers,William R. Jarnagin,Maureen McHugo,Sandy Napel,Eugene Vorontsov,Lena Maier-Hein,M. Jorge Cardoso +23 more
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TLDR
A large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain.Abstract:
Semantic segmentation of medical images aims to associate a pixel with a label in a medical image without human initialization. The success of semantic segmentation algorithms is contingent on the availability of high-quality imaging data with corresponding labels provided by experts. We sought to create a large collection of annotated medical image datasets of various clinically relevant anatomies available under open source license to facilitate the development of semantic segmentation algorithms. Such a resource would allow: 1) objective assessment of general-purpose segmentation methods through comprehensive benchmarking and 2) open and free access to medical image data for any researcher interested in the problem domain. Through a multi-institutional effort, we generated a large, curated dataset representative of several highly variable segmentation tasks that was used in a crowd-sourced challenge - the Medical Segmentation Decathlon held during the 2018 Medical Image Computing and Computer Aided Interventions Conference in Granada, Spain. Here, we describe these ten labeled image datasets so that these data may be effectively reused by the research community.read more
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The future of digital health with federated learning
Nicola Rieke,Nicola Rieke,Jonny Hancox,Wenqi Li,Fausto Milletari,Holger R. Roth,Shadi Albarqouni,Shadi Albarqouni,Spyridon Bakas,Mathieu N. Galtier,Bennett A. Landman,Klaus H. Maier-Hein,Klaus H. Maier-Hein,Sebastien Ourselin,Micah J. Sheller,Ronald M. Summers,Andrew Trask,Daguang Xu,Maximilian Baust,M. Jorge Cardoso +19 more
TL;DR: In this article, the authors consider key factors contributing to this issue, explore how federated learning may provide a solution for the future of digital health and highlight the challenges and considerations that need to be addressed.
Journal ArticleDOI
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Micah J. Sheller,Brandon Edwards,G. Anthony Reina,Jason Martin,Sarthak Pati,Aikaterini Kotrotsou,Mikhail Milchenko,Weilin Xu,Daniel S. Marcus,Rivka R. Colen,Spyridon Bakas +10 more
TL;DR: It is shown that federated learning among 10 institutions results in models reaching 99% of the model quality achieved with centralized data, and the effects of data distribution across collaborating institutions on model quality and learning patterns are investigated.
Journal ArticleDOI
The Future of Digital Health with Federated Learning
Nicola Rieke,Nicola Rieke,Jonny Hancox,Wenqi Li,Fausto Milletari,Holger R. Roth,Shadi Albarqouni,Shadi Albarqouni,Spyridon Bakas,Mathieu N. Galtier,Bennett A. Landman,Klaus H. Maier-Hein,Klaus H. Maier-Hein,Sebastien Ourselin,Micah J. Sheller,Ronald M. Summers,Andrew Trask,Daguang Xu,Maximilian Baust,M. Jorge Cardoso +19 more
TL;DR: This paper considers key factors contributing to this issue, explores how federated learning (FL) may provide a solution for the future of digital health and highlights the challenges and considerations that need to be addressed.
Journal ArticleDOI
Deep semantic segmentation of natural and medical images: a review
TL;DR: This review categorizes the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis- based, loss function-based, sequenced models, weakly supervised, and multi-task methods.
Book ChapterDOI
TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation
Yundong Zhang,Huiye Liu,Qiang Hu +2 more
TL;DR: TransFuse as discussed by the authors combines Transformers and CNNs in a parallel style, where both global dependency and low-level spatial details can be efficiently captured in a much shallower manner.
References
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TL;DR: A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
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Fully Convolutional Networks for Semantic Segmentation
TL;DR: Fully convolutional networks (FCN) as mentioned in this paper were proposed to combine semantic information from a deep, coarse layer with appearance information from shallow, fine layer to produce accurate and detailed segmentations.
Journal ArticleDOI
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
Bjoern H. Menze,Andras Jakab,Stefan Bauer,Jayashree Kalpathy-Cramer,Keyvan Farahani,Justin Kirby,Yuliya Burren,N Porz,Johannes Slotboom,Roland Wiest,Levente Lanczi,Elizabeth R. Gerstner,Marc-André Weber,Tal Arbel,Brian B. Avants,Nicholas Ayache,Patricia Buendia,D. Louis Collins,Nicolas Cordier,Jason J. Corso,Antonio Criminisi,Tilak Das,Hervé Delingette,Çağatay Demiralp,Christopher R. Durst,Michel Dojat,Senan Doyle,Joana Festa,Florence Forbes,Ezequiel Geremia,Ben Glocker,Polina Golland,Xiaotao Guo,Andac Hamamci,Khan M. Iftekharuddin,Raj Jena,Nigel M. John,Ender Konukoglu,Danial Lashkari,José Mariz,Raphael Meier,Sérgio Pereira,Doina Precup,Stephen J. Price,Tammy Riklin Raviv,Syed M. S. Reza,Michael Ryan,Duygu Sarikaya,Lawrence H. Schwartz,Hoo-Chang Shin,Jamie Shotton,Carlos A. Silva,Nuno Sousa,Nagesh K. Subbanna,Gábor Székely,Thomas J. Taylor,Owen M. Thomas,Nicholas J. Tustison,Gozde Unal,Flor Vasseur,Max Wintermark,Dong Hye Ye,Liang Zhao,Binsheng Zhao,Darko Zikic,Marcel Prastawa,Mauricio Reyes,Koen Van Leemput +67 more
TL;DR: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) as mentioned in this paper was organized in conjunction with the MICCAI 2012 and 2013 conferences, and twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low and high grade glioma patients.
Journal ArticleDOI
The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Kenneth W. Clark,Bruce A. Vendt,Kirk E. Smith,John Freymann,Justin Kirby,Paul Koppel,Stephen M. Moore,Stanley R. Phillips,David R. Maffitt,Michael Pringle,Lawrence Tarbox,Fred W. Prior +11 more
TL;DR: The management tasks and user support model for TCIA is described, an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer.
Journal ArticleDOI
The three-dimensional organization of the hippocampal formation: a review of anatomical data.
David G. Amaral,Menno P. Witter +1 more
TL;DR: It is concluded that it is heuristically most reasonable to consider the hippocampal formation as a three-dimensional cortical region with important information processing taking place in both the transverse and long axes.