Fatty liver accurate quantitative analysis method and device, computer equipment and storage medium

Abstract

The invention provides an accurate quantitative analysis method for fatty liver. The method comprises the following steps: acquiring a to-be-analyzed liver three-dimensional image; obtaining a liver segmentation result of the to-be-analyzed liver three-dimensional image; carrying out quantitative analysis of liver fat and segmented fat: calculating the fat distribution uniformity, the average fat content, the median fat content and the confidence interval of the whole liver; and calculating the average fat content, the median fat content and the confidence interval of each liver segment in the liver segmentation result. The invention further provides a related device, computer equipment and a storage medium. The deep learning technology is used for achieving automatic liver segmentation and segmented fat quantitative result analysis, fat range early warning is achieved through fat content quantitative analysis, in addition, perivascular fat extraction and quantitative analysis are achieved, and therefore more comprehensive and accurate quantitative analysis on the fatty liver is achieved, and a doctor can be helped to more accurately judge the fat deposition degree and the treatment effect of the fatty liver.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to the technical field of quantitative analysis of fatty liver, and specifically refers to a method, device, computer equipment and storage medium for precise quantitative analysis of fatty liver. Background technique [0002] Fatty liver has surpassed hepatitis B to become the number one chronic liver disease in my country, and its incidence is on the rise. Without intervention, it may deteriorate into steatohepatitis, liver cirrhosis, hepatocellular carcinoma, and even liver failure leading to death. [0003] The qualitative diagnosis of fatty liver can be done regardless of ultrasound, CT, MRI, and MRS. At present, fatty liver screening is mainly realized through B-ultrasound and CT images. B-ultrasound is simple and economical, but it is greatly affected by factors such as individual differences in patients, testing equipment, and doctor's experience. Al...

AI Technical Summary

Problems solved by technology

[0005] However, the quantitative analysis of the existing fatty liver quantitative analysis technology is relatively limited, and there is no quantitative analysis from the perspective of liver segmentation. The quantitative analysis of liver segmentation and segmental fat is not comprehensive enough, and there is a lack of fat range warning and perivascular fat. Quantitative analysis cannot be applied in clinical practice

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