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

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Title
Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs
Author(s)
Jisu Hong; Bo-yong Park; Mi Ji Lee; Chin-Sang Chung; Jihoon Cha; Hyunjin Park
Publication Date
2020-01
Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, v.183, no., pp.105068 -
Publisher
ELSEVIER IRELAND LTD
Abstract
Background and Objective: Patients with migraine show an increased presence of white matter hyperin- tensities (WMHs), especially deep WMHs. Segmentation of small, deep WMHs is a critical issue in man- aging 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 F 1 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. ©2019 Elsevier B.V. All rights reserved.
URI
https://pr.ibs.re.kr/handle/8788114/6254
ISSN
0169-2607
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > Journal Papers (저널논문)
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