Deep supervision with additional labels for retinal vessel segmentation task

TL;DR

This paper introduces a deep neural network approach with edge-aware multi-class labeling and deep supervision for improved retinal vessel segmentation, achieving high accuracy and efficiency across multiple datasets.

Contribution

The paper proposes a novel deep learning method incorporating boundary-aware labels and residual U-net with deep supervision for enhanced vessel segmentation.

Findings

Achieved 97.99% AUC on DRIVE dataset.

Improved tiny vessel detection accuracy.

Comparable performance with state-of-the-art methods.

Abstract

Automatic analysis of retinal blood images is of vital importance in diagnosis tasks of retinopathy. Segmenting vessels accurately is a fundamental step in analysing retinal images. However, it is usually difficult due to various imaging conditions, low image contrast and the appearance of pathologies such as micro-aneurysms. In this paper, we propose a novel method with deep neural networks to solve this problem. We utilize U-net with residual connection to detect vessels. To achieve better accuracy, we introduce an edge-aware mechanism, in which we convert the original task into a multi-class task by adding additional labels on boundary areas. In this way, the network will pay more attention to the boundary areas of vessels and achieve a better performance, especially in tiny vessels detecting. Besides, side output layers are applied in order to give deep supervision and therefore help convergence. We train and evaluate our model on three databases: DRIVE, STARE, and CHASEDB1. Experimental results show that our method has a comparable performance with AUC of 97.99% on DRIVE and an efficient running time compared to the state-of-the-art methods.