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Abstract #2824

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

Lesion count, which encodes the lesion historical information, is an important biomarker for diagnosis and treatment of multiple sclerosis. Confluent lesions pose a great challenge to traditional automated methods, as these lesions are connected spatially, which requires expert experience to separate them. In this abstract, we propose a Hough voting method based on deep neural networks to resolve the issue. Experimental results on an in-house dataset demonstrates the superiority of our approach.

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