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

doi:10.1038/s41598-020-64803-w

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

Department of Bio-Medical Engineering

Research output: Contribution to journal › Article › peer-review

129 Scopus citations

Abstract

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 language	English
Article number	8242
Journal	Scientific Reports
Volume	10
Issue number	1
DOIs	https://doi.org/10.1038/s41598-020-64803-w
State	Published - 1 Dec 2020

Funding

Funders	Funder number
National Institutes of Health
National Institute of Mental Health	R24-MH114799
National Institute of Neurological Disorders and Stroke	R01-NS070906, R01-NS085211, R21NS093349, R01-NS094456, R01-NS082347
National Institute of Biomedical Imaging and Bioengineering	R01-EB017255
NATIONAL MULTIPLE SCLEROSIS SOCIETY	RG-1507-05243, RG-1707-28586

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Carass, A., Roy, S., Gherman, A., Reinhold, J. C., Jesson, A., Arbel, T., Maier, O., Handels, H., Ghafoorian, M., Platel, B., Birenbaum, A., Greenspan, H., Pham, D. L., Crainiceanu, C. M., Calabresi, P. A., Prince, J. L., Roncal, W. R. G., Shinohara, R. T., & Oguz, I. (2020). Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis. Scientific Reports, 10(1), Article 8242. https://doi.org/10.1038/s41598-020-64803-w

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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",

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Carass, A, Roy, S, Gherman, A, Reinhold, JC, Jesson, A, Arbel, T, Maier, O, Handels, H, Ghafoorian, M, Platel, B, Birenbaum, A, Greenspan, H, Pham, DL, Crainiceanu, CM, Calabresi, PA, Prince, JL, Roncal, WRG, Shinohara, RT & Oguz, I 2020, 'Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis', Scientific Reports, vol. 10, no. 1, 8242. https://doi.org/10.1038/s41598-020-64803-w

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AU - Ghafoorian, Mohsen

AU - Platel, Bram

AU - Birenbaum, Ariel

AU - Greenspan, Hayit

AU - Pham, Dzung L.

AU - Crainiceanu, Ciprian M.

AU - Calabresi, Peter A.

AU - Prince, Jerry L.

AU - Roncal, William R.Gray

AU - Shinohara, Russell T.

AU - Oguz, Ipek

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Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis

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