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Abstract #3493

A semi-supervised level-set loss for white matter hyperintensities segmentation on FLAIR without manual labels

Fan Huang1, Peng Xia1, Varut Vardhanabhuti1, Edward Sai-Kam Hui2, Gary Kui-Kai Lau3, Henry Ka-Fung Mak1, and Peng Cao1
1Department of Diagnostic Radiology, The University of Hong Kong, Hong Kong, Hong Kong, 2Department of Rehabilitation Science, The Hong Kong Polytechnic University, Hong Kong, Hong Kong, 3Department of Medicine, The University of Hong Kong, HongKong, Hong Kong

We propose a semi-supervised training scheme for white matter hyperintensity (WMHs) segmentation using V-Net on FLAIR images. The training procedure does not require manual labeling data but only a few domain knowledge of WMHs. The segmentation result obtained by the V-Net with the proposed scheme outperformed that obtained by the supervised loss with manual labels, showing great potential and generalizability in medical image applications.

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