Retinal Vascular Segmentation Based on Depth-Separable Convolution and Attention Mechanisms.

Retinal vascular segmentation is an important research direction in the field of medical image processing, its main purpose is to automatically segment the vascular area from the fundus image, and provide doctors with more accurate diagnosis results and treatment plans. In recent years, with the continuous development of deep learning technology, retinal vascular segmentation algorithm based on deep learning has gradually become a research hotspot. In this paper, the retinal vascular segmentation algorithm based on deep learning is mainly improved, and the retinal vascular segmentation algorithm based on IPN-V2 is improved, in an attempt to make new explorations. The retinal vascular segmentation algorithm based on IPN-V2 provides global information, but requires a large amount of image data and label information, the image size is different, and most importantly, the accuracy of the model for the segmentation of the original image is not enough. Therefore, this paper improves the retinal vascular segmentation algorithm based on IPN-V2, introduces the attention mechanism, and constructs a retinal vascular segmentation model based on ASR-IPN-V2, which enables the model to extract more image details from the original image through the depth-separable convolution and convolutional block attention mechanisms. Experiments show that the retinal vascular segmentation model based on ASR-IPN-V2 greatly improves the efficiency of retinal vascular segmentation.