U-Net model-based medical image segmentation method and apparatus, and storage medium

Abstract

The invention relates to a U-Net model-based medical image segmentation method and apparatus, and a storage medium. The method comprises the steps of determining a target segmentation area of a plurality of medical images; respectively carrying out medical scanning on the target segmentation areas of the plurality of medical images to obtain color medical image samples; respectively preprocessingeach color medical image sample to obtain a gray image after the G channel is extracted; respectively carrying out noise removal operation on each gray image, and respectively generating a corresponding segmentation label image according to each gray image after noise removal; performing at least one data enhancement processing operation of rotation, translation and scaling on the medical image sample and the segmented label image to obtain a plurality of bitmap samples; dividing each bitmap sample into a training set and a verification set; inputting each training set into a medical image segmentation model to train the medical image segmentation model; debugging the model parameter by using each verification set to obtain an optimal model parameter; and performing performance testing byusing each verification set pair to obtain the optimal segmentation accuracy.

Description

technical field [0001] The present invention relates to the technical field of big data deep learning, in particular to a method, device and storage medium for segmenting medical images based on the U-Net model. Background technique [0002] With the rapid development of medical imaging technology, big data is now used to analyze medical images, dig out useful information from massive medical images, and then identify medical images to determine whether a patient is sick or to determine the type of disease of a patient. However, due to the large number of medical images and the wide variety of equipment used to collect clinical medical images, as well as different diseased parts and different types of diseases, the current data analysis methods cannot accurately extract information from various medical images in these environments. Useful information can be unearthed, and the current data analysis methods can no longer meet the current medical needs. Contents of the invent...

AI Technical Summary

Problems solved by technology

However, due to the large number of medical images and the wide variety of equipment for collecting clinical medical images, coupled with different diseased parts and different types of diseases, the current data analysis methods cannot accurately extract information from various medical images in these environments. useful information, and the current data analysis methods can no longer meet the current medical needs

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