A Systematic Review of Deep Learning Approaches for Vessel Segmentation in Retinal Fundus Images

Govardhan Hegde, Srikanth Prabhu, Shourya Gupta, Gautham Manuru Prabhu, Anshita Palorkar, Metta Venkata Srujan, Sulatha V. Bhandary

Research output: Contribution to journalConference articlepeer-review


Retinal vessel segmentation (RVS) is important to accurately differentiate retinal vasculature for diagnosing and monitoring various ocular and systemic diseases. The traditional methods for RVS have mostly involved supervised learning, although semi-supervised and unsupervised techniques are on the rise. This paper reviews the increase in complexity of developments in RVS primarily after 2020. The methods were chosen to cover both the gradual transition over time and a variety of unorthodox or combinatorial approaches. This includes convolutional neural networks, encoder-decoder models, generative models, and other multi-modal or hybrid techniques. CNN approaches discussed employ Zero Phase Component Analysis, Global Contrast Normalization, and reinforcement learning. Encoder-decoder models include approaches such as the use of skip and residual connections, spatial attention, and atrous enhancement U-Net. Generative models propose short link connections, recurrent residual blocks, and multi-scale features to refine convolutional blocks. Hybrid methods involve the use of connectivity features, the MISODATA Algorithm, cross-domain adaptation, and multiple filters (such as morphological, match, and Gabor). All the frameworks are compared based on their performance on the benchmark dataset DRIVE to provide a comprehensive understanding of the current state of RVS.

Original languageEnglish
Article number012021
JournalJournal of Physics: Conference Series
Issue number1
Publication statusPublished - 2023
Event2nd International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2023 - Manipal, India
Duration: 16-02-202317-02-2023

All Science Journal Classification (ASJC) codes

  • General Physics and Astronomy


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