Method for segmenting liver and focus thereof in medical image

Abstract

The invention relates to a method for segmenting a liver and a focus thereof in a medical image, and the method comprises the steps: firstly carrying out the screening and integration preprocessing ofabdominal CT image data, dividing the abdominal CT image data into a plurality of data sets with different purposes, building a new neural network, and carrying out the initial training through employing small image data; then, storing the trained model, carrying out secondary training by using the original image and a new data enhancement mode, carrying out expansion and corrosion processing onthe predicted image, and evaluating by using a medical evaluation index; through the model prediction results trained by DL, GDL and TL loss functions, adding and averaging the prediction results of the three loss models to form a fusion feature; finally, modifying the network, wherein the three loss models are fused in a single network for training prediction. End-to-end training test can be carried out, liver and focus can be identified at the same time with high precision and high speed, doctors are effectively helped to identify CT images, time and energy consumed by doctors are greatly reduced, and the probability of misdiagnosis is reduced.

Description

technical field [0001] The invention relates to a method for segmenting the liver and its focus in medical images. Background technique [0002] At present, liver disease is one of the parts with high morbidity and mortality in the world. However, if liver disease occurs in the early stage, and the lesion can be located in time, the lesion can be controlled and defended, and the metastasis of the lesion can be avoided. The treatment of liver disease is of great significance. The emergence of CT images has greatly improved the diagnosis level of doctors, but it requires a doctor with a deep professional background and rich clinical experience to locate the lesion, and it is very time-consuming to diagnose the patient's disease. With the rapid development of computer vision technology, segmentation algorithms such as region-based, threshold segmentation and machine learning have appeared. The research on image semantic segmentation has made great progress. Medical image segme...

AI Technical Summary

Problems solved by technology

There are several problems that need to be solved on the basis of U-Net. U-Net generally adopts a five-layer structure, which can be solved in a shallow layer for simple data, and can be optimized for complex data deepening networks. Multi-deep networks are most suitable for unresolved problems; The importance of layers is not clear, and how deep the network needs to be is not pointed out; only through the short connections of each layer, deep and shallow features cannot be effectively integrated

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