Journal ArticleDOI
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
Tobias Heimann,B. van Ginneken,Martin Styner,Yulia Arzhaeva,V. Aurich,C. Bauer,A. Beck,C. Becker,Reinhard Beichel,G. Bekes,Fernando Bello,G. Binnig,Horst Bischof,Alexander Bornik,P. Cashman,Ying Chi,A. Cordova,Benoit M. Dawant,Marta Fidrich,Jacob D. Furst,D. Furukawa,Lars Grenacher,Joachim Hornegger,D. Kainmuller,Richard I. Kitney,H. Kobatake,Hans Lamecker,T. Lange,Jeongjin Lee,B. Lennon,Rui Li,Senhu Li,Hans-Peter Meinzer,Gábor Németh,Daniela Raicu,A.-M. Rau,E.M. van Rikxoort,Mikael Rousson,L. Rusko,K.A. Saddi,G. Schmidt,D. Seghers,Akinobu Shimizu,Pieter Slagmolen,Erich Sorantin,G. Soza,R. Susomboon,Jonathan M. Waite,A. Wimmer,Ivo Wolf +49 more
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TLDR
A comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.Abstract:
This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.read more
Citations
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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
elastix : A Toolbox for Intensity-Based Medical Image Registration
TL;DR: The software consists of a collection of algorithms that are commonly used to solve medical image registration problems, and allows the user to quickly configure, test, and compare different registration methods for a specific application.
Journal ArticleDOI
Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge.
Geert Litjens,Robert Toth,Wendy J. M. van de Ven,Caroline M. A. Hoeks,Sjoerd Kerkstra,Bram van Ginneken,G.R. Vincent,Gwenael Guillard,Neil Birbeck,Jindang Zhang,Robin Strand,Filip Malmberg,Yangming Ou,Christos Davatzikos,Matthias Kirschner,Florian Jung,Jing Yuan,Wu Qiu,Qinquan Gao,Philip J. Edwards,Bianca Maan,Ferdinand van der Heijden,Soumya Ghose,Soumya Ghose,Jhimli Mitra,Jhimli Mitra,Jason Dowling,Dean C. Barratt,Henkjan J. Huisman,Anant Madabhushi +29 more
TL;DR: Although average algorithm performance was good to excellent and the Imorphics algorithm outperformed the second observer on average, it is shown that algorithm combination might lead to further improvement, indicating that optimal performance for prostate segmentation is not yet obtained.
Posted Content
The Liver Tumor Segmentation Benchmark (LiTS)
Patrick Bilic,Patrick Ferdinand Christ,Eugene Vorontsov,Grzegorz Chlebus,Hao Chen,Qi Dou,Chi-Wing Fu,Xiao Han,Pheng-Ann Heng,Jürgen Hesser,Samuel Kadoury,Tomasz Konopczynski,Miao Le,Chunming Li,Xiaomeng Li,Jana Lipkova,John Lowengrub,Hans Meine,Jan Hendrik Moltz,Chris Pal,Marie Piraud,Xiaojuan Qi,Jin Qi,Markus Rempfler,Karsten Roth,Andrea Schenk,Anjany Sekuboyina,Ping Zhou,Christian Hülsemeyer,Marcel Beetz,Florian Ettlinger,Felix Gruen,Georgios Kaissis,Fabian Lohöfer,Rickmer Braren,Julian Walter Holch,Felix Hofmann,Wieland H. Sommer,Volker Heinemann,Colin Jacobs,Gabriel Efrain Humpire Mamani,Bram van Ginneken,Gabriel Chartrand,An Tang,Michal Drozdzal,Avi Ben-Cohen,Eyal Klang,Marianne M. Amitai,Eli Konen,Hayit Greenspan,Johan Moreau,Alexandre Hostettler,Luc Soler,Refael Vivanti,Adi Szeskin,Naama Lev-Cohain,Jacob Sosna,Leo Joskowicz,Bjoern H. Menze +58 more
TL;DR: The set-up and results of the Liver Tumor Segmentation Benchmark (LITS) organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2016 and International Conference on Medical Image Computing Computer Assisted Intervention (MICCAI) 2017 are reported.
Book ChapterDOI
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Patrick Ferdinand Christ,Mohamed Ezzeldin A. Elshaer,Florian Ettlinger,Sunil Tatavarty,Marc Bickel,Patrick Bilic,Markus Rempfler,Marco Armbruster,Felix Hofmann,Melvin D'Anastasi,Wieland H. Sommer,Seyed-Ahmad Ahmadi,Bjoern H. Menze +12 more
TL;DR: In this paper, a method to automatically segment liver and lesions in CT abdomen images using cascaded fully convolutional neural networks (CFCNs) and dense 3D conditional random fields (CRFs) is presented.
References
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Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Journal ArticleDOI
Nearest neighbor pattern classification
Thomas M. Cover,Peter E. Hart +1 more
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.