Slice image processing method and device, computer equipment and storage medium

Abstract

The invention relates to a slice image processing method and device, computer equipment and a storage medium. The method comprises the following steps: segmenting a slice image into a plurality of cell images according to acquired image segmentation parameters; inputting the cell image into the trained convolutional neural network model; obtaining classification probability data, and according tothe classification probability data, determining a classification category corresponding to the cell image, then counting the number of cell images of an abnormal category in the slice image, calculating the proportion data of the counted number of cell images in the total number of cell images, and when the proportion data is greater than a preset proportion threshold, marking the slice image asan abnormal slice image. On one hand, detailed analysis is carried out by segmenting the slice image. The overall analysis accuracy of the slice image is improved. On the other hand, classification accuracy of a single cell image is improved by adopting an artificial intelligence convolutional neural network, so that the analysis accuracy of the slice image is improved.

Description

Technical field [0001] This application relates to the field of image processing technology, and in particular to a slice image processing method, device, computer equipment and storage medium. Background technique [0002] With the development of medical technology, the identification of various types of cells in pathological slice images plays an important role in medical treatment. In recent years, many medical research teams at home and abroad have begun to work on the identification of various types of cells in ascites. By screening various types of cells in the pathological slice image, the diseased cells can be found in time, thereby providing auxiliary support for the grading of the pathological slice. The traditional pathological section grading method uses manual grading. The pathologist moves the pathological section and then scans the entire pathological section with the naked eye to identify the types of cells in the pathological section, and then identify the pathol...

AI Technical Summary

Problems solved by technology

[0003] However, since there are a large number of diseased cells in each pathological slice image, various cell types, complex cell structures, and diverse cell shapes, the identification and classification of abnormal cells depend...

Images