Chen, Chunhui and Chuah, Joon Huang and Ali, Raza and Wang, Yizhou (2021) Retinal vessel segmentation using deep learning: A review. IEEE Access, 9. pp. 111985-112004. ISSN 2169-3536, DOI https://doi.org/10.1109/ACCESS.2021.3102176.
Full text not available from this repository.Abstract
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.
| Item Type: | Article |
|---|---|
| Funders: | University of Malaya Faculty Research Grant (GPF009A-2018) |
| Uncontrolled Keywords: | Image segmentation; Retinal vessels; Deep learning; Convolution; Feature extraction; Biomedical imaging; Kernel; Retinal vessel segmentation; fundus images; deep learning; convolutional neural network |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 04 Apr 2022 04:13 |
| Last Modified: | 04 Apr 2022 04:13 |
| URI: | http://eprints.um.edu.my/id/eprint/26644 |
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