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Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks

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收录情况： ◇ SCIE ◇ EI

作者：
Shi, Feng[1] * ; Yang, Qi[1,2] ; Guo, Xiuhai[3] ; Qureshi, Touseef Ahmad[1] ; Tian, Zixiao[1] ; Miao, Huijuan[3] ; Dey, Amini[1,4] ; Li, Debiao[1,4,5] ; Fan, Zhaoyang[1,4,5] ;
机构： [1]Biomedical Imaging Research Institute, Cedars- Sinai Medical Center, Los Angeles, CA 90048 USA [2]Department of Radiology, Xuanwu Hospital. [3]Department of Neurology, Xuanwu Hospital. [4]Department of Medicine,University of California, Los Angeles, Los Angeles, CA 90095 USA [5]Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095 USA
出处：
DOI： 10.1109/TBME.2019.2896972
ISSN：

关键词： Vessel wall imaging deep learning segmentation quantification intracranial atherosclerotic disease

摘要：
Objective: To develop an automated vessel wall segmentation method using convolutional neural networks to facilitate the quantification on magnetic resonance (MR) vessel wall images of patients with intracranial atherosclerotic disease (ICAD). Methods: Vessel wall images of 56 subjects were acquired with our recently developed whole-brain three-dimensional (3-D) MR vessel wall imaging (VWI) technique. An intracranial vessel analysis (IVA) framework was presented to extract, straighten, and resample the interested vessel segment into 2-D slices. A U-net-like fully convolutional networks (FCN) method was proposed for automated vessel wall segmentation by hierarchical extraction of low- and high-order convolutional features. Results: The network was trained and validated on 1160 slices and tested on 545 slices. The proposed segmentation method demonstrated satisfactory agreement with manual segmentations with Dice coefficient of 0.89 for the lumen and 0.77 for the vessel wall. The method was further applied to a clinical study of additional 12 symptomatic and 12 asymptomatic patients with >50% ICAD stenosis at the middle cerebral artery (MCA). Normalized wall index at the focal MCA ICAD lesions was found significantly larger in symptomatic patients compared to asymptomatic patients. Conclusion: We have presented an automated vessel wall segmentation method based on FCN as well as the IVA framework for 3-D intracranial MR VWI. Significance: This approach would make large-scale quantitative plaque analysis more realistic and promote the adoption of MR VWI in ICAD management.

基金：
American Heart AssociationAmerican Heart Association [15SDG25710441]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [NHLBI 2R01HL096119, NHLBI 1R01HL147355]; Natural Science Foundation of ChinaNational Natural Science Foundation of China [61375112]

基金编号： 15SDG25710441 NHLBI 2R01HL096119 NHLBI 1R01HL147355 61375112

语种：
外文
被引次数：
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PubmedID：
中科院(CAS)分区：
出版当年[2018]版：
大类 | 2 区 工程技术
小类 | 2 区 工程：生物医学
最新[2023]版：
大类 | 2 区 医学
小类 | 2 区 工程：生物医学
JCR分区：
出版当年[2017]版：
Q1 ENGINEERING, BIOMEDICAL
最新[2023]版：
Q2 ENGINEERING, BIOMEDICAL

影响因子： 最新[2023版] 最新五年平均 出版当年[2017版] 出版当年五年平均 出版前一年[2016版] 出版后一年[2018版]

第一作者：
第一作者机构： [1]Biomedical Imaging Research Institute, Cedars- Sinai Medical Center, Los Angeles, CA 90048 USA
通讯作者：
通讯机构： [1]Biomedical Imaging Research Institute, Cedars- Sinai Medical Center, Los Angeles, CA 90048 USA [4]Department of Medicine,University of California, Los Angeles, Los Angeles, CA 90095 USA [5]Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095 USA
推荐引用方式(GB/T 7714)：
Shi Feng,Yang Qi,Guo Xiuhai,et al.Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks[J].IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING.2019,66(10):2840-2847.doi:10.1109/TBME.2019.2896972.
APA：
Shi, Feng,Yang, Qi,Guo, Xiuhai,Qureshi, Touseef Ahmad,Tian, Zixiao...&Fan, Zhaoyang.(2019).Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks.IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING,66,(10)
MLA：
Shi, Feng,et al."Intracranial Vessel Wall Segmentation Using Convolutional Neural Networks".IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 66..10(2019):2840-2847

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