Channel Attention Residual U-Net for Retinal Vessel Segmentation

TL;DR

This paper introduces CAR-UNet, a novel deep learning model with channel attention mechanisms, achieving state-of-the-art retinal vessel segmentation accuracy across multiple datasets.

Contribution

The paper proposes a new model, CAR-UNet, incorporating MECA and CADRB modules to improve retinal vessel segmentation performance.

Findings

Achieved state-of-the-art results on DRIVE, CHASE DB1, and STARE datasets.

Enhanced feature discrimination through MECA in skip connections.

Effective integration of residual blocks with channel attention improves segmentation accuracy.

Abstract

Retinal vessel segmentation is a vital step for the diagnosis of many early eye-related diseases. In this work, we propose a new deep learning model, namely Channel Attention Residual U-Net (CAR-UNet), to accurately segment retinal vascular and non-vascular pixels. In this model, we introduced a novel Modified Efficient Channel Attention (MECA) to enhance the discriminative ability of the network by considering the interdependence between feature maps. On the one hand, we apply MECA to the "skip connections" in the traditional U-shaped networks, instead of simply copying the feature maps of the contracting path to the corresponding expansive path. On the other hand, we propose a Channel Attention Double Residual Block (CADRB), which integrates MECA into a residual structure as a core structure to construct the proposed CAR-UNet. The results show that our proposed CAR-UNet has reached the state-of-the-art performance on three publicly available retinal vessel datasets: DRIVE, CHASE DB1 and STARE.