MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation

TL;DR

This paper introduces MC-UNet, a novel U-shaped neural network with multi-module concatenation, atrous convolution, and multi-kernel pooling for improved retinal blood vessel segmentation, especially microvessels.

Contribution

The paper proposes a new multi-module concatenation U-Net architecture incorporating atrous convolution and multi-kernel pooling for enhanced retinal vessel segmentation.

Findings

Effective segmentation on DRIVE, STARE, and CHASE_DB1 datasets.

Improved microvessel detection accuracy.

Outperforms existing methods in key metrics.

Abstract

Accurate segmentation of the blood vessels of the retina is an important step in clinical diagnosis of ophthalmic diseases. Many deep learning frameworks have come up for retinal blood vessels segmentation tasks. However, the complex vascular structure and uncertain pathological features make the blood vessel segmentation still very challenging. A novel U-shaped network named Multi-module Concatenation which is based on Atrous convolution and multi-kernel pooling is put forward to retinal vessels segmentation in this paper. The proposed network structure retains three layers the essential structure of U-Net, in which the atrous convolution combining the multi-kernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with dense atrous convolution module and multi-kernel pooling module to form a multi-module concatenation. And different dilation rates are selected by cascading to acquire a larger receptive field in atrous convolution. Adequate comparative experiments are conducted on these public retinal datasets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method is effective, especially for microvessels. The code will be put out at https://github.com/Rebeccala/MC-UNet