Brain Vessel Segmentation Using Deep Learning-A Review

被引:11
作者
Goni, Mohammad Raihan [1 ]
Ruhaiyem, Nur Intan Raihana [1 ]
Mustapha, Muzaimi [2 ]
Achuthan, Anusha [1 ]
Nassir, Che Mohd Nasril Che Mohd [2 ,3 ,4 ]
机构
[1] Univ Sains Malaysia, Sch Comp Sci, Gelugor 11800, Penang, Malaysia
[2] UniversitiSainsMalaysia, Sch Med Sci, Dept Neurosci, Kubang Kerian 16150, Kelantan, Malaysia
[3] Univ Kebangsaan, Dept Radiol, Malaysia Med Ctr, Kuala Lumpur 56000, Malaysia
[4] Neuro Psychol & Islamic Res & Consultancy Pty Ltd, ZA-7764 Cape Town, South Africa
来源
IEEE ACCESS | 2022年 / 10卷
关键词
Image segmentation; Biomedical imaging; Deep learning; Three-dimensional displays; Magnetic resonance imaging; Hypertension; Brain modeling; Brain vessel segmentation; convolutional neural network; deep learning; magnetic resonance angiogram; CONVOLUTIONAL NEURAL-NETWORK; CEREBROVASCULAR SEGMENTATION; 3D; IMAGES; MODEL; ANEURYSMS; DISEASE; MRI;
D O I
10.1109/ACCESS.2022.3214987
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article provides a comprehensive review of deep learning-based blood vessel segmentation of the brain. Cerebrovascular disease develops when blood arteries in the brain are compromised, resulting in severe brain injuries such as ischemic stroke, brain hemorrhages, and many more. Early detection enables patients to obtain more effective treatment before becoming critically unwell. Due to the superior efficiency and accuracy compared to manual segmentation and other computer-assisted diagnosis procedures, deep learning algorithms have been extensively deployed in brain vascular segmentation. This study examined current articles on deep learning-based brain vascular segmentation, which examined the proposed methodologies, particularly the network architectures, and determined the model trend. We evaluated challenges and crucial factors associated with the application of deep learning to brain vascular segmentation, as well as future research prospects. This paper will assist researchers in developing more sophisticated and robust models in the future to develop deep learning solutions.
引用
收藏
页码:111322 / 111336
页数:15
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