Improvement of Retinal Vessel Segmentation Method Based on U-Net

Retinal vessel segmentation remains a challenging task because the morphology of the retinal vessels reflects the health of a person, which is essential for clinical diagnosis. Therefore, achieving accurate segmentation of the retinal vessel shape can determine the patient's physical condition in a timely manner and can prevent blindness in patients. Since the traditional retinal vascular segmentation method is manually operated, this can be time-consuming and laborious. With the development of convolutional neural networks, U-shaped networks (U-Nets) and variants show good performance in image segmentation. However, U-Net is prone to feature loss due to the operation of the encoder convolution layer and also causes the problem of mismatch in the processing of contextual information features caused by the skip connection part. Therefore, we propose an improvement of the retinal vessel segmentation method based on U-Net to segment retinal vessels accurately. In order to extract more features from encoder features, we replace the convolutional layer with ResNest network structure in feature extraction, which aims to enhance image feature extraction. In addition, a Depthwise FCA Block (DFB) module is proposed to deal with the mismatched processing of local contextual features by skip connections. Combined with the two public datasets on retinal vessel segmentation, namely DRIVE and CHASE_DB1, and comparing our method with a larger number of networks, the experimental results confirmed the effectiveness of the proposed method. Our method is better than most segmentation networks, demonstrating the method's significant clinical value.