White matter hyperintensities segmentation using an ensemble of neural networks
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- Xinxin Li
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
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- Yu Zhao
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
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- Jiyang Jiang
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry UNSW Sydney New South Wales Australia
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- Jian Cheng
- Beijing Advanced Innovation Center for Big Data‐Based Precision Medicin, School of Computer Science and Engineering Beihang University Beijing China
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- Wanlin Zhu
- Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University Beijng China
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- Zhenzhou Wu
- BioMind Technology AI Center, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital Beijng China
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- Jing Jing
- Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University Beijng China
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- Zhe Zhang
- Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University Beijng China
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- Wei Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry UNSW Sydney New South Wales Australia
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- Perminder S. Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry UNSW Sydney New South Wales Australia
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- Yongjun Wang
- Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University Beijng China
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- Tao Liu
- Key Laboratory of Biomechanics and Mechanobiology, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering Beihang University Beijing China
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- Zixiao Li
- Neuroimaging Center of Excellence, China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University Beijng China
Description
<jats:title>Abstract</jats:title><jats:p>White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of WMHs are important clinical measures. We present a pipeline using deep fully convolutional network and ensemble models, combining U‐Net, SE‐Net, and multi‐scale features, to automatically segment WMHs and estimate their volumes and locations. We evaluated our method in two datasets: a clinical routine dataset comprising 60 patients (selected from Chinese National Stroke Registry, CNSR) and a research dataset composed of 60 patients (selected from MICCAI WMH Challenge, MWC). The performance of our pipeline was compared with four freely available methods: LGA, LPA, UBO detector, and U‐Net, in terms of a variety of metrics. Additionally, to access the model generalization ability, another research dataset comprising 40 patients (from Older Australian Twins Study and Sydney Memory and Aging Study, OSM), was selected and tested. The pipeline achieved the best performance in both research dataset and the clinical routine dataset with DSC being significantly higher than other methods (<jats:italic>p</jats:italic> < .001), reaching .833 and .783, respectively. The results of model generalization ability showed that the model trained on the research dataset (DSC = 0.736) performed higher than that trained on the clinical dataset (DSC = 0.622). Our method outperformed widely used pipelines in WMHs segmentation. This system could generate both image and text outputs for whole brain, lobar and anatomical automatic labeling WMHs. Additionally, software and models of our method are made publicly available at <jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.nitrc.org/projects/what_v1">https://www.nitrc.org/projects/what_v1</jats:ext-link>.</jats:p>
Journal
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- Human Brain Mapping
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Human Brain Mapping 43 (3), 929-939, 2021-10-27
Wiley
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Details 詳細情報について
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- CRID
- 1360017286857631232
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- ISSN
- 10970193
- 10659471
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- Data Source
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- Crossref