• Jisu Hong, Bo-Yong Park, Mi Ji Lee, Chin-Sang Chung, Jihoon Cha, and Hyunjin Park.
• Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea; Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea.
• Comput Methods Programs Biomed. 2020 Jan 1; 183: 105065.

Background And ObjectivePatients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in managing migraine care. Here, we aim to develop a novel approach to segmenting deep WMHs using deep neural networks based on the U-Net.Methods148 non-elderly subjects with migraine were recruited for this study. Our model consists of two networks: the first identifies potential deep WMH candidates, and the second reduces the false positives within the candidates. The first network for initial segmentation includes four down-sampling layers and four up-sampling layers to sort the candidates. The second network for false positive reduction uses a smaller field-of-view and depth than the first network to increase utilization of local information.ResultsOur proposed model segments deep WMHs with a high true positive rate of 0.88, a low false discovery rate of 0.13, and F1 score of 0.88 tested with ten-fold cross-validation. Our model was automatic and performed better than existing models based on conventional machine learning.ConclusionWe developed a novel segmentation framework tailored for deep WMHs using U-Net. Our algorithm is open-access to promote future research in quantifying deep WMHs and might contribute to the effective management of WMHs in migraineurs.Copyright © 2019. Published by Elsevier B.V.

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