0 90

Cited 0 times in

Two-step Deep Neural Network for Segmentation of Deep White Matter Hyperintensities in Migraineurs

Authors
 Jisu Hong  ;  Bo-Yong Park  ;  Mi Ji Lee  ;  Chin-Sang Chung  ;  Jihoon Cha  ;  Hyunjin Park 
Citation
 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol.183 : e105065, 2020-01 
Journal Title
 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 
ISSN
 0169-2607 
Issue Date
2020-01
Keywords
Deep neural network ; Deep white matter hyperintensity ; Migraine ; Segmentation
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.
Full Text
https://www.sciencedirect.com/science/article/pii/S0169260719305851
DOI
10.1016/j.cmpb.2019.105065
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Cha, Jihoon(차지훈)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/175949
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links