A Survey of Deep Learning for Retinal Blood Vessel Segmentation Methods: Taxonomy, Trends, Challenges and Future Directions

Abstract

Recent advancements in deep learning architectures have extended their application to computer vision tasks, one of which is the segmentation of retinal blood vessels from retinal fundus images. This is a problem that has piqued researchers’ interest in recent times. This paper presents a review of the taxonomy and analysis of enhancement techniques used in recent works to modify and optimize the performance of deep learning retinal blood vessels segmentation methods. The objectives of this study are to critically review the taxonomies of the state-of-the-art deep learning retinal blood vessels segmentation methods, observe the trends of the enhancement techniques of recent work, identify the challenges, and suggest potential future research directions. The taxonomies focused on in this paper include optimization algorithms, regularization methods, pooling operations, activation functions, transfer learning, and ensemble learning methods. In doing this, 110 relevant papers spanning the years 2016 to 2021 are reviewed. The findings could aid future research plans, while the suggested ideas would improve the predictive accuracy of future models for automatic retinal blood vessels segmentation algorithms with good generalization ability and optimal performance.