Multiple Sclerosis brain lesion segmentation with different architecture ensembles

Pouria Tohidi; Samuel W. Remedios; Danielle L. Greenman; Muhan Shao; Shuo Han; Blake E. Dewey; Jacob C. Reinhold; Yi-Yu Chou; Dzung L. Pham; Jerry L. Prince; Aaron Carass

doi:10.1117/12.2623302

4 April 2022 Multiple Sclerosis brain lesion segmentation with different architecture ensembles

Pouria Tohidi, Samuel W. Remedios, Danielle L. Greenman, Muhan Shao, Shuo Han, Blake E. Dewey, Jacob C. Reinhold, Yi-Yu Chou, Dzung L. Pham, Jerry L. Prince, Aaron Carass

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203625 (2022) https://doi.org/10.1117/12.2623302
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

White matter lesion (WML) segmentation applied to magnetic resonance imaging (MRI) scans of people with multiple sclerosis has been an area of extensive research in recent years. As with most tasks in medical imaging, deep learning (DL) methods have proven very effective and have quickly replaced existing methods. Despite the improvement offered by these networks, there are still shortcomings with these DL approaches. In this work, we compare several DL algorithms, as well as methods for ensembling the results of those algorithms, for performing MS lesion segmentation. An ensemble approach is shown to best estimate total WML and has the highest agreement with manual delineations.

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Pouria Tohidi, Samuel W. Remedios, Danielle L. Greenman, Muhan Shao, Shuo Han, Blake E. Dewey, Jacob C. Reinhold, Yi-Yu Chou, Dzung L. Pham, Jerry L. Prince, and Aaron Carass "Multiple Sclerosis brain lesion segmentation with different architecture ensembles", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203625 (4 April 2022); https://doi.org/10.1117/12.2623302

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