A review of retinal vessel segmentation for fundus image analysis

The fundus is the only region where arteries, veins and capillaries can be directly observed. Morphological changes of retinal vessels in the fundus are signals for the appearance of many fundus and cardiovascular diseases. Consequently, the segmentation of retinal vessels is crucial for diagnosing and screening various diseases. In recent years, a large amount of research publications has been published on retinal vessel segmentation. This paper offers a comprehensive review of retinal vessel automatic segmentation research, covering both traditional methods and deep learning methods, including unsupervised and supervised learning methods. Especially, the supervised learning methods are summarized and analyzed from three aspects: traditional CNN-based, GAN-based, and UNet-based methods. This paper also presents an overview of the development of retinal vessel automatic segmentation and analyzes the advantages and disadvantages of existing segmentation methods. The results are shown in two at-a-glance tables. Finally, our work provides a faster and better look to recognize and understand the field of retinal vessel segmentation.

Automated summary

This paper offers a comprehensive review of retinal vessel automatic segmentation research, including both traditional methods and deep learning methods. In particular, supervised learning methods are summarized and analyzed based on CNN, GAN, and UNet. The advantages and disadvantages of existing segmentation methods are also outlined.