MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation
This work addresses the challenge of segmenting complex retinal blood vessels for clinical ophthalmic diagnosis, representing an incremental improvement over existing deep learning methods.
The authors tackled retinal blood vessel segmentation by proposing MC-UNet, a U-shaped network that integrates atrous convolution, multi-kernel pooling, and spatial attention modules to capture contextual information, achieving effective results particularly for microvessels on datasets like DRIVE, STARE, and CHASE_DB1.
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