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Comput Methods Programs Biomed. 2019 Sep 5;183:105065. doi: 10.1016/j.cmpb.2019.105065. [Epub ahead of print]

Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.

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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.
Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon 16419, South Korea; School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon 16419, South Korea. Electronic address:



Patients 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.


148 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.


Our 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.


We 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.


Deep neural network; Deep white matter hyperintensity; Migraine; Segmentation


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