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Longitudinal multiple sclerosis lesion segmentation: Resource and challenge.

Authors :
Carass, Aaron
Roy, Snehashis
Jog, Amod
Cuzzocreo, Jennifer L.
Magrath, Elizabeth
Gherman, Adrian
Button, Julia
Nguyen, James
Prados, Ferran
Sudre, Carole H.
Jorge Cardoso, Manuel
Cawley, Niamh
Ciccarelli, Olga
Wheeler-Kingshott, Claudia A.M.
Ourselin, Sébastien
Catanese, Laurence
Deshpande, Hrishikesh
Maurel, Pierre
Commowick, Olivier
Barillot, Christian
Source :
NeuroImage. Mar2017, Vol. 148, p77-102. 26p.
Publication Year :
2017

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 website 2 2 The Challenge Evaluation Website is: http://smart-stats-tools.org/lesion-challenge-2015 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
10538119
Volume :
148
Database :
Academic Search Index
Journal :
NeuroImage
Publication Type :
Academic Journal
Accession number :
121559131
Full Text :
https://doi.org/10.1016/j.neuroimage.2016.12.064