Literature DB >> 28087490

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

Aaron Carass1, Snehashis Roy2, Amod Jog3, Jennifer L Cuzzocreo4, Elizabeth Magrath2, Adrian Gherman5, Julia Button4, James Nguyen4, Ferran Prados6, Carole H Sudre7, Manuel Jorge Cardoso8, Niamh Cawley9, Olga Ciccarelli9, Claudia A M Wheeler-Kingshott9, Sébastien Ourselin8, Laurence Catanese10, Hrishikesh Deshpande10, Pierre Maurel10, Olivier Commowick10, Christian Barillot10, Xavier Tomas-Fernandez11, Simon K Warfield11, Suthirth Vaidya12, Abhijith Chunduru12, Ramanathan Muthuganapathy12, Ganapathy Krishnamurthi12, Andrew Jesson13, Tal Arbel13, Oskar Maier14, Heinz Handels14, Leonardo O Iheme15, Devrim Unay15, Saurabh Jain16, Diana M Sima16, Dirk Smeets16, Mohsen Ghafoorian17, Bram Platel18, Ariel Birenbaum19, Hayit Greenspan20, Pierre-Louis Bazin21, Peter A Calabresi4, Ciprian M Crainiceanu5, Lotta M Ellingsen22, Daniel S Reich23, Jerry L Prince24, Dzung L Pham2.   

Abstract

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Magnetic resonance imaging; Multiple sclerosis

Mesh:

Year:  2017        PMID: 28087490      PMCID: PMC5344762          DOI: 10.1016/j.neuroimage.2016.12.064

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  91 in total

1.  Adaptive, template moderated, spatially varying statistical classification.

Authors:  S K Warfield; M Kaus; F A Jolesz; R Kikinis
Journal:  Med Image Anal       Date:  2000-03       Impact factor: 8.545

2.  Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation.

Authors:  Simon K Warfield; Kelly H Zou; William M Wells
Journal:  IEEE Trans Med Imaging       Date:  2004-07       Impact factor: 10.048

3.  Probabilistic segmentation of white matter lesions in MR imaging.

Authors:  Petronella Anbeek; Koen L Vincken; Matthias J P van Osch; Robertus H C Bisschops; Jeroen van der Grond
Journal:  Neuroimage       Date:  2004-03       Impact factor: 6.556

4.  Spatial decision forests for MS lesion segmentation in multi-channel MR images.

Authors:  Ezequiel Geremia; Bjoern H Menze; Olivier Clatz; Ender Konukoglu; Antonio Criminisi; Nicholas Ayache
Journal:  Med Image Comput Comput Assist Interv       Date:  2010

5.  Morphometric analysis of white matter lesions in MR images: method and validation.

Authors:  A P Zijdenbos; B M Dawant; R A Margolin; A C Palmer
Journal:  IEEE Trans Med Imaging       Date:  1994       Impact factor: 10.048

6.  An open source multivariate framework for n-tissue segmentation with evaluation on public data.

Authors:  Brian B Avants; Nicholas J Tustison; Jue Wu; Philip A Cook; James C Gee
Journal:  Neuroinformatics       Date:  2011-12

7.  Multiple sclerosis lesion segmentation using an automatic multimodal graph cuts.

Authors:  Daniel García-Lorenzo; Jeremy Lecoeur; Douglas L Arnold; D Louis Collins; Christian Barillot
Journal:  Med Image Comput Comput Assist Interv       Date:  2009

8.  Evolution of the blood-brain barrier in newly forming multiple sclerosis lesions.

Authors:  María I Gaitán; Colin D Shea; Iordanis E Evangelou; Roger D Stone; Kaylan M Fenton; Bibiana Bielekova; Luca Massacesi; Daniel S Reich
Journal:  Ann Neurol       Date:  2011-06-27       Impact factor: 10.422

9.  Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects.

Authors:  R Kikinis; C R Guttmann; D Metcalf; W M Wells; G J Ettinger; H L Weiner; F A Jolesz
Journal:  J Magn Reson Imaging       Date:  1999-04       Impact factor: 4.813

10.  MR imaging intensity modeling of damage and repair in multiple sclerosis: relationship of short-term lesion recovery to progression and disability.

Authors:  D S Meier; H L Weiner; C R G Guttmann
Journal:  AJNR Am J Neuroradiol       Date:  2007 Nov-Dec       Impact factor: 3.825

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  47 in total

1.  Locally adaptive magnetic resonance intensity models for unsupervised segmentation of multiple sclerosis lesions.

Authors:  Alfiia Galimzianova; Žiga Lesjak; Daniel L Rubin; Boštjan Likar; Franjo Pernuš; Žiga Špiclin
Journal:  J Med Imaging (Bellingham)       Date:  2017-11-01

2.  MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

Authors:  Alessandra M Valcarcel; Kristin A Linn; Simon N Vandekar; Theodore D Satterthwaite; John Muschelli; Peter A Calabresi; Dzung L Pham; Melissa Lynne Martin; Russell T Shinohara
Journal:  J Neuroimaging       Date:  2018-03-08       Impact factor: 2.486

3.  Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm.

Authors:  T D Nguyen; S Zhang; A Gupta; Y Zhao; S A Gauthier; Y Wang
Journal:  AJNR Am J Neuroradiol       Date:  2018-03-08       Impact factor: 3.825

4.  7T MPFLAIR versus MP2RAGE for Quantifying Lesion Volume in Multiple Sclerosis.

Authors:  Margaret Spini; Seongjin Choi; Daniel M Harrison
Journal:  J Neuroimaging       Date:  2020-06-22       Impact factor: 2.486

Review 5.  Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis.

Authors:  Davood Karimi; Haoran Dou; Simon K Warfield; Ali Gholipour
Journal:  Med Image Anal       Date:  2020-06-20       Impact factor: 8.545

6.  Automated Detection and Segmentation of Multiple Sclerosis Lesions Using Ultra-High-Field MP2RAGE.

Authors:  Mário João Fartaria; Pascal Sati; Alexandra Todea; Ernst-Wilhelm Radue; Reza Rahmanzadeh; Kieran OʼBrien; Daniel S Reich; Meritxell Bach Cuadra; Tobias Kober; Cristina Granziera
Journal:  Invest Radiol       Date:  2019-06       Impact factor: 6.016

7.  Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: A large-scale study.

Authors:  Refaat E Gabr; Ivan Coronado; Melvin Robinson; Sheeba J Sujit; Sushmita Datta; Xiaojun Sun; William J Allen; Fred D Lublin; Jerry S Wolinsky; Ponnada A Narayana
Journal:  Mult Scler       Date:  2019-06-13       Impact factor: 6.312

8.  Lesion location matters: The relationships between white matter hyperintensities on cognition in the healthy elderly.

Authors:  Leonie Lampe; Shahrzad Kharabian-Masouleh; Jana Kynast; Katrin Arelin; Christopher J Steele; Markus Löffler; A Veronica Witte; Matthias L Schroeter; Arno Villringer; Pierre-Louis Bazin
Journal:  J Cereb Blood Flow Metab       Date:  2017-11-06       Impact factor: 6.200

9.  Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure.

Authors:  Olivier Commowick; Audrey Istace; Michaël Kain; Baptiste Laurent; Florent Leray; Mathieu Simon; Sorina Camarasu Pop; Pascal Girard; Roxana Améli; Jean-Christophe Ferré; Anne Kerbrat; Thomas Tourdias; Frédéric Cervenansky; Tristan Glatard; Jérémy Beaumont; Senan Doyle; Florence Forbes; Jesse Knight; April Khademi; Amirreza Mahbod; Chunliang Wang; Richard McKinley; Franca Wagner; John Muschelli; Elizabeth Sweeney; Eloy Roura; Xavier Lladó; Michel M Santos; Wellington P Santos; Abel G Silva-Filho; Xavier Tomas-Fernandez; Hélène Urien; Isabelle Bloch; Sergi Valverde; Mariano Cabezas; Francisco Javier Vera-Olmos; Norberto Malpica; Charles Guttmann; Sandra Vukusic; Gilles Edan; Michel Dojat; Martin Styner; Simon K Warfield; François Cotton; Christian Barillot
Journal:  Sci Rep       Date:  2018-09-12       Impact factor: 4.379

10.  Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation.

Authors:  Ipek Oguz; Aaron Carass; Dzung L Pham; Snehashis Roy; Nagesh Subbana; Peter A Calabresi; Paul A Yushkevich; Russell T Shinohara; Jerry L Prince
Journal:  Brainlesion       Date:  2018-02-17
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