CM-Unet: A Convolutional Neural Network for Retinal Vessel Segmentation

Accurate segmentation of retinal vessels from retinal images is critical for the detection and diagnosis of many ocular diseases and associated cardiovascular diseases. However, the complex structure of retinal vessels, the low contrast between the background and the vessels, and the various noises generated by medical imaging systems make retinal vessel segmentation a great challenge. To address these challenges, we propose an architecture called CM-Unet for accurate segmentation of retinal blood vessels by improving the U-Net medical image segmentation model in this paper. Specifically, we achieve cross-layer cascading and reuse of features by constructing dense connections in the encoder part. In addition, we introduce global context blocks to filter noise in low-level semantic information and suppress irrelevant features to make the network more focused on the target feature region. Meanwhile, we use depthwise separable convolution instead of standard convolution to reduce the number of parameters. Experimental results of vascular segmentation on DRIVE and ARIA datasets show that the proposed method has significantly improved the segmentation accuracy demonstrating its effectiveness in vascular segmentation tasks.