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An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.

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收录情况： ◇ SCIE ◇ EI

作者：
Li Liang [a,b] * ; Pengzheng Zhou [b] ; Wanxin Lu [a] ; Xutao Guo [a,b] ; Chenfei Ye [b] ; Haiyan Lv [c] ; Wang Tong[a] ; Ting Ma [a,b,d,e,*1] ;
机构： [a]School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China [b]Peng Cheng Laboratory, Shenzhen, Guangdong, China [c]Mindsgo Life Science Shenzhen Ltd, Shenzhen, Guangdong, China [d]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China [e]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
出处：
DOI： 10.1016/j.compmedimag.2021.101873
ISSN：

摘要：
Recent studies have confirmed that white matter hyperintensities (WMHs) accumulated in strategic brain regions can predict cognitive impairments associated with Alzheimer's disease (AD). The knowledge of white matter anatomy facilitates lesion-symptom mapping associated with cognition, and provides important spatial information for lesion segmentation algorithms. However, deep learning-based methods in the white matter hyperintensity (WMH) segmentation realm do not take full advantage of anatomical knowledge in decision-making and lesion localization processes. In this paper, we proposed an anatomical knowledge-based MRI deep learning pipeline (AU-Net), handcrafted anatomical-based spatial features developed from brain atlas were integrated with a well-designed U-Net configuration to simultaneously segment and locate WMHs. Manually annotated data from WMH segmentation challenge were used for the evaluation. We then applied this pipeline to investigate the association between WMH burden and cognition within another publicly available database. The results showed that AU-Net significantly improved segmentation performance compared with methods that did not incorporate anatomical knowledge (p < 0.05), and achieved a 14-17% increase based on area under the curve (AUC) in the cohort with mild WMH burden. Different configurations for incorporating anatomical knowledge were evaluated, the proposed stage-wise AU-Net-two-step method achieved the best performance (Dice: 0.86, modified Hausdorff distance: 3.06 mm), which was comparable with the state-of-the-art method (Dice: 0.87, modified Hausdorff distance: 3.62 mm). We observed different WMH accumulation patterns associated with normal aging and cognitive impairments. Furthermore, the characteristics of individual WMH lesions located in strategic regions (frontal and parietal subcortical white matter, as well as corpus callosum) were significantly correlated with cognition after adjusting for total lesion volumes. Our findings suggest that AU-Net is a reliable method to segment and quantify brain WMHs in elderly cohorts, and is valuable in individual cognition evaluation. Copyright © 2021 Elsevier Ltd. All rights reserved.

基金：
grants from National Key Research and Development Program of China (2018YFC1312000), the National Natural Science Foundation of China (61801145), and the Basic Research Project of Shenzhen Science and Technology Program (JCYJ20180306171800589).
语种：
外文
被引次数：
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PubmedID：
中科院(CAS)分区：
出版当年[2020]版：
大类 | 3 区 医学
小类 | 3 区 工程：生物医学 3 区 核医学
最新[2025]版：
大类 | 2 区 医学
小类 | 2 区 工程：生物医学 2 区 核医学
JCR分区：
出版当年[2019]版：
Q1 ENGINEERING, BIOMEDICAL Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
最新[2024]版：
Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Q2 ENGINEERING, BIOMEDICAL

影响因子： 最新[2024版] 最新五年平均 出版当年[2019版] 出版当年五年平均 出版前一年[2018版] 出版后一年[2020版]

第一作者：
第一作者机构： [a]School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China [b]Peng Cheng Laboratory, Shenzhen, Guangdong, China
通讯作者：
通讯机构： [a]School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China [b]Peng Cheng Laboratory, Shenzhen, Guangdong, China [d]National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, China [e]Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China [*1]School of Electronic and Information Engineering, Harbin Institute of Technology at Shenzhen, Shenzhen, Guangdong, China
推荐引用方式(GB/T 7714)：
Li Liang,Pengzheng Zhou,Wanxin Lu,et al.An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.[J].COMPUTERIZED MEDICAL IMAGING AND GRAPHICS.2021,89:101873.doi:10.1016/j.compmedimag.2021.101873.
APA：
Li Liang,Pengzheng Zhou,Wanxin Lu,Xutao Guo,Chenfei Ye...&Ting Ma.(2021).An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment..COMPUTERIZED MEDICAL IMAGING AND GRAPHICS,89,
MLA：
Li Liang,et al."An anatomical knowledge-based MRI deep learning pipeline for white matter hyperintensity quantification associated with cognitive impairment.".COMPUTERIZED MEDICAL IMAGING AND GRAPHICS 89.(2021):101873

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