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, Tomas-Fernandez Xavier, Warfield Simon K, Vaidya Suthirth, Chunduru Abhijith, Muthuganapathy Ramanathan, Krishnamurthi Ganapathy, Jesson Andrew, Arbel Tal, Maier Oskar, Handels Heinz, Iheme Leonardo O, Unay Devrim, Jain Saurabh, Sima Diana M, Smeets Dirk, Ghafoorian Mohsen, Platel Bram, Birenbaum Ariel, Greenspan Hayit, Bazin Pierre-Louis, Calabresi Peter A, Crainiceanu Ciprian M, Ellingsen Lotta M, Reich Daniel S, Prince Jerry L, Pham Dzung L
Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
CNRM, The Henry M. Jackson Foundation for the Advancement of Military Medicine, Bethesda, MD 20892, USA.
Neuroimage. 2017 Mar 1;148:77-102. doi: 10.1016/j.neuroimage.2016.12.064. Epub 2017 Jan 11.
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 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.
我们与2015年国际生物医学影像学会(ISBI)会议联合组织了一次纵向病变分割挑战赛,为注册参与者提供训练和测试数据。训练数据包括五名受试者,平均有4.4个时间点;测试数据包括十四名受试者,平均有4.4个时间点。所有82个数据集都有由两名人类专家评分者勾勒出的与多发性硬化症相关的白质病变。十一个团队使用最先进的病变分割算法提交了挑战赛结果,其中十个团队在会议上展示了他们的结果。我们进行了定量评估,比较了两名评分者的一致性,并除了三种其他病变分割算法外,还探索了十一个提交结果的性能。这次挑战赛带来了三个独特的机会:(1)共享丰富的数据集;(2)社区中正在进行的各种研究途径的合作与比较;(3)对当前使用的评估指标进行审查和完善。我们报告了挑战赛参与者的表现,以及共识勾勒的构建和评估。图像数据和手动勾勒将继续通过一个评估网站供下载,作为该领域未来研究人员的资源。这个数据资源提供了一个平台,以公平和一致的方式相互比较现有方法以及与多个手动评分者进行比较。