Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis

Aaron Carass*, Snehashis Roy, Adrian Gherman, Jacob C. Reinhold, Andrew Jesson, Tal Arbel, Oskar Maier, Heinz Handels, Mohsen Ghafoorian, Bram Platel, Ariel Birenbaum, Hayit Greenspan, Dzung L. Pham, Ciprian M. Crainiceanu, Peter A. Calabresi, Jerry L. Prince, William R.Gray Roncal, Russell T. Shinohara, Ipek Oguz

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

104 Scopus citations


The Sørensen-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.

Original languageEnglish
Article number8242
JournalScientific Reports
Issue number1
StatePublished - 1 Dec 2020


FundersFunder number
National Institutes of Health
National Institute of Mental HealthR24-MH114799
National Institute of Neurological Disorders and StrokeR01-NS070906, R01-NS085211, R21NS093349, R01-NS094456, R01-NS082347
National Institute of Biomedical Imaging and BioengineeringR01-EB017255
National Multiple Sclerosis SocietyRG-1507-05243, RG-1707-28586


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