Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs
Computer methods and programs in biomedicine, 2020•Elsevier
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 …
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 …
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.
Elsevier
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