| Literature DB >> 28087490 |
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.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