A high resolution representation network with multi-path scale for retinal vessel segmentation

Abstract

Background and objectives: Automatic retinal vessel segmentation (RVS) in fundus images is expected to be a vital step in the early image diagnosis of ophthalmologic diseases. However, it is a challenging task to detect the retinal vessel accurately mainly due to the vascular intricacies, lesion areas and optic disc edges in retinal fundus images.

Methods: In this paper, we propose a high resolution representation network with multi-path scale (MPS-Net) for RVS aiming to improve the performance of extracting the retinal blood vessels. In the MPS-Net, there exist one high resolution main road and two lower resolution branch roads where the proposed multi-path scale modules are embedded to enhance the representation ability of network. Besides, in order to guide the network focus on learning the features of hard examples in retinal images, we design a hard-focused cross-entropy loss function.

Results: We evaluate our network structure on DRIVE, STARE, CHASE and synthetic images and the quantitative comparisons with respect to the existing methods are presented. The experimental results show that our approach is superior to most methods in terms of F1-score, sensitivity, G-mean and Matthews correlation coefficient.

Conclusions: The promising segmentation performances reveal that our method has potential in real-world applications and can be exploited for other medical images with further analysis.

Keywords: Deep learning; High resolution; Multi-path scale; Retinal vessels segmentation.