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MS-Voter: Learning Where to Vote for Confluent Multiple Sclerosis Lesion Separation

Hang Zhang¹, Jinwei Zhang¹, Junghun Cho¹, Susan A. Gauthier¹, Pascal Spincemaille¹, Thanh D. Nguyen¹, and Yi Wang¹
¹Cornell University, New York, NY, United States

A machine learning technique is described for separating confluent lesions in multiple sclerosis using Hough voting and K-means clustering.

Figure 1. Example Illustration of how to compute lesion offsets and voxel weight based on ground-truth lesion labels. Individual lesions are marked by masks of different colors.

Figure 2. Qualitative comparison between baseline methods and our proposed MS-Voter.