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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