@article{d05c789c37bd494f8459f18d37359d17,
title = "Evaluating White Matter Lesion Segmentations with Refined S{\o}rensen-Dice Analysis",
abstract = "The S{\o}rensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.",
author = "Aaron Carass and Snehashis Roy and Adrian Gherman and Reinhold, {Jacob C.} and Andrew Jesson and Tal Arbel and Oskar Maier and Heinz Handels and Mohsen Ghafoorian and Bram Platel and Ariel Birenbaum and Hayit Greenspan and Pham, {Dzung L.} and Crainiceanu, {Ciprian M.} and Calabresi, {Peter A.} and Prince, {Jerry L.} and Roncal, {William R.Gray} and Shinohara, {Russell T.} and Ipek Oguz",
note = "Publisher Copyright: {\textcopyright} 2020, The Author(s).",
year = "2020",
month = dec,
day = "1",
doi = "10.1038/s41598-020-64803-w",
language = "אנגלית",
volume = "10",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Research",
number = "1",
}