MC-UNet: Multimodule Concatenation Based on U-Shape Network for Retinal Blood Vessels Segmentation

Abstract

Accurate retinal blood vessels segmentation is an important step in the 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 blood vessel segmentation still very challenging. This paper proposes a novel multimodule concatenation via a U-shaped network for retinal vessels segmentation, which is based on atrous convolution and multikernel pooling. The proposed network structure retains three layers of the essential structure of U-Net, in which the atrous convolution combining the multikernel pooling blocks are designed to obtain more contextual information. The spatial attention module is concatenated with the dense atrous convolution module and the multikernel pooling module to form a multimodule 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 released at https://github.com/rocklijun/MC-UNet.

CMP-UNet: A Retinal Vessel Segmentation Network Based on Multi-Scale Feature Fusion

“…In addition, many other researchers have contributed significantly to automatic retinal vessel segmentation. Li et al proposed a novel multimodule concatenation method using a U-shaped network that combines atrous convolution [27] with multikernel pooling blocks to obtain more contextual information [28]. Deng et al [29] proposed a segmentation model based on multi-scale attention with residual mechanism [30], D-Mnet, combined with the improved PCNN [31] model to unite the advantages of supervised and unsupervised learning [32].…”