Abstract: Medical images have small gray-scale changes,and segmentation targets and backgrounds are not easy to distinguish,thus image segmentation is full of challenging problems.Most of the existing models unify the segmented high-frequency edges with the low-frequency subjects for learning,ignoring the difference between high-frequency information and low-frequency information and the difference in the proportion of both in the image.To address this problem,edge guided V-shape network(EGV-Net),a multi-scale convolutional neural network based on edge guidance,is proposed to perform targeted learning from two feature perspectives:low-frequency segmented subjects and high-frequency segmented edges.Among them,the low-frequency features are passed through the feature transfer by the encoder-decoder connection method to learn the main part of the segmentation target.The high-frequency features are firstly extracted from the segmentation mapping by edge extraction method,and then the segmentation edges are filtered and separated from it.The segmented edges of high frequency are guided by edge guidance module to make accurate segmentation of low frequency segmented edges and recover edge detail accuracy.Experimental results in liver images and ISIC2016 show that the proposed algorithm has better control over the overall segmentation and better segmentation effect at the edge details than other models.

Key words: Deep learning, Medical image segmentation, Multi-scale features, Edge extraction, Edge guidance