Longitudinal multiple sclerosis lesion segmentation: Resource and challenge - Archive ouverte HAL Access content directly
Journal Articles NeuroImage Year : 2017

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

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Saurabh Jain
  • Function : Author
Diana M. Sima
  • Function : Author
Dirk Smeets
  • Function : Author

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.
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Dates and versions

inserm-01480156 , version 1 (23-05-2017)

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Aaron Carass, Snehashis Roy, Amod Jog, Jennifer L. Cuzzocreo, Elizabeth Magrath, et al.. Longitudinal multiple sclerosis lesion segmentation: Resource and challenge. NeuroImage, 2017, 148, pp.77 - 102. ⟨10.1016/j.neuroimage.2016.12.064⟩. ⟨inserm-01480156⟩
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