SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss

TL;DR

SPNet introduces a shared decoder and pyramid-like loss to improve retinal vessel segmentation, especially for capillaries and edges, demonstrating superior accuracy and generalization on benchmark datasets.

Contribution

The paper proposes a novel deep neural network with shared decoder and pyramid-like loss for enhanced retinal vessel segmentation, focusing on fine structures.

Findings

Outperforms state-of-the-art methods on public benchmarks.

Excels in segmenting capillaries and vessel contours.

Shows strong generalization across different datasets.

Abstract

Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently, convolutional neural networks have shown significant ability to extract the blood vessel structure. However, it remains challenging to refined segmentation for the capillaries and the edges of retinal vessels due to thickness inconsistencies and blurry boundaries. In this paper, we propose a novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss (SPNet) to address the above problems. Specifically, we introduce a decoder-sharing mechanism to capture multi-scale semantic information, where feature maps at diverse scales are decoded through a sequence of weight-sharing decoder modules. Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase. A pyramid-like loss function is designed to compensate possible segmentation errors progressively. Experimental results on public benchmarks show that the proposed method outperforms the backbone network and the state-of-the-art methods, especially in the regions of the capillaries and the vessel contours. In addition, performances on cross-datasets verify that SPNet shows stronger generalization ability.