Retinal Vessel Segmentation Using Deep Learning: A Review

被引:72
作者
Chen, Chunhui [1 ]
Chuah, Joon Huang [1 ]
Ali, Raza [1 ,2 ]
Wang, Yizhou [3 ]
机构
[1] Univ Malaya, Dept Elect Engn, Fac Engn, Kuala Lumpur 50603, Malaysia
[2] BUITEMS, Fac Informat & Commun Technol, Quetta 87300, Pakistan
[3] Peking Univ, Ctr Frontiers Comp Studies, Beijing 100871, Peoples R China
来源
IEEE ACCESS | 2021年 / 9卷
关键词
Image segmentation; Retinal vessels; Deep learning; Convolution; Feature extraction; Biomedical imaging; Kernel; Retinal vessel segmentation; fundus images; deep learning; convolutional neural network; CONVOLUTIONAL NEURAL-NETWORK; BLOOD-VESSELS; FUNDUS IMAGES; NET; ARCHITECTURE; ENHANCEMENT; ALGORITHM; CONTRAST; WAVELET;
D O I
10.1109/ACCESS.2021.3102176
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 paper presents a comprehensive review of retinal blood vessel segmentation based on deep learning. The geometric characteristics of retinal vessels reflect the health status of patients and help to diagnose some diseases such as diabetes and hypertension. The accurate diagnosis and timing treatment of these diseases can prevent global blindness of patients. Recently, deep learning algorithms have been rapidly applied to retinal vessel segmentation due to their higher efficiency and accuracy, when compared with manual segmentation and other computer-aided diagnosis techniques. In this work, we reviewed recent publications for retinal vessel segmentation based on deep learning. We surveyed these proposed methods especially the network architectures and figured out the trend of models. We summarized obstacles and key aspects for applying deep learning to retinal vessel segmentation and indicated future research directions. This article will help researchers to construct more advanced and robust models.
引用
收藏
页码:111985 / 112004
页数:20
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