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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.

Author information

1
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.
2
Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, South Korea.
3
Department of Radiology, Severance Hospital, Research Institute of Radiological Science, Yonsei University College of Medicine, Seoul 03722, Republic of Korea.
4
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: hyunjinp@skku.edu.

Abstract

BACKGROUND AND OBJECTIVE:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.

KEYWORDS:

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

PMID:
31522090
DOI:
10.1016/j.cmpb.2019.105065

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