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Longitudinal multiple sclerosis lesion segmentation: Resource and challenge
- Source :
- NeuroImage, NeuroImage, 2017, 148, pp.77-102. ⟨10.1016/j.neuroimage.2016.12.064⟩, NeuroImage, Elsevier, 2017, 148, pp.77-102. ⟨10.1016/j.neuroimage.2016.12.064⟩, NeuroImage, 148, 77-102, NeuroImage, 148, pp. 77-102
- Publication Year :
- 2016
-
Abstract
- Contains fulltext : 173122.pdf (Publisher’s version ) (Closed access) 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.
- Subjects :
- Adult
Male
medicine.medical_specialty
Multiple Sclerosis
Cognitive Neuroscience
computer.software_genre
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
Consistency (database systems)
0302 clinical medicine
Resource (project management)
Imaging, Three-Dimensional
medicine
Image Processing, Computer-Assisted
Humans
Medical physics
Segmentation
[SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Longitudinal Studies
Multiple sclerosis lesion
[SDV.IB] Life Sciences [q-bio]/Bioengineering
Observer Variation
Lesion segmentation
Training set
business.industry
Data Science
Middle Aged
Magnetic Resonance Imaging
White Matter
Women's cancers Radboud Institute for Health Sciences [Radboudumc 17]
Data set
Neurology
[SDV.IB]Life Sciences [q-bio]/Bioengineering
[SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Female
Data mining
business
computer
030217 neurology & neurosurgery
Algorithms
Test data
Subjects
Details
- ISSN :
- 10959572 and 10538119
- Volume :
- 148
- Database :
- OpenAIRE
- Journal :
- NeuroImage
- Accession number :
- edsair.doi.dedup.....857d25c5987d571f7d394c1bb9b0290f