Liver image semantic segmentation method based on edge attention strategy

Abstract

The invention discloses a liver image semantic segmentation method based on an edge attention strategy, and the method comprises the steps: S1, carrying out the format conversion of an original data set; s2, preprocessing the liver image data; s3, analyzing edge features of the liver image and designing an Encoder-Decoder deep learning framework model, wherein the Encoder-Decoder deep learning framework model comprises an encoding stage and a decoding stage; s4, in a coding stage, obtaining an edge attention graph by utilizing a ResNet34 residual network and an edge attention strategy module;s5, in a decoding stage, performing feature extraction and segmented image generation by utilizing a deconvolution and edge attention strategy module; and S6, performing noise reduction processing onthe image after semantic segmentation. Through training and fusion of the edge attention strategy module and the deep convolutional network, the loss function is optimized, the depth features of the liver image are extracted and segmented, and the method has the characteristic of improving the semantic segmentation effect of the liver image.

Description

technical field [0001] The invention relates to a method for semantic segmentation of medical images, in particular to a method for semantic segmentation of liver images based on an edge attention strategy. Background technique [0002] The liver is an organ in the body that mainly performs metabolic functions, and it is the largest organ in the human body. Currently, for the diagnosis of liver disease, MRI of the liver is the most common. The main judgment for disease analysis is to accurately identify a large number of continuous liver parts, and there will be some unavoidable problems in this, such as being affected by factors such as subjective experience, cognitive ability, and fatigue. Computer medical image recognition helps to improve the accuracy and stability of recognition. [0003] In recent years, deep learning technology has made great progress in dealing with various computer vision tasks, especially the Convolution Neural Network (CNN) for image classificat...

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Problems solved by technology

However, these studies did not do further research on the details of the liver edge, so further work is needed on this aspect

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