Esophageal cancer pathological image labeling method

Abstract

The esophageal cancer pathological image labeling method comprises the following steps: a) performing dyeing correction processing on an esophageal pathological image subjected to H & E dyeing; b) labeling expert canceration areas; c) mapping the canceration area outline marked by the expert into a pathological image of 40X; d) constructing an epithelial tissue contour detection model; d-1) marking whether pixel points belong to an epithelial region, an interstitial tissue or an irrelevant blank region; (d-2) constructing an end-to-end convolutional neural network model; and e) fusing the labeling areas. According to the method, the epithelial tissue contour is automatically drawn in the labeling process according to the characteristic that the esophageal cancer morbidity area occurs in the epithelial tissue basal layer area, so that the time cost of expert labeling is greatly saved. The method only aims at esophageal pathological section image modeling; only the edge of the epithelialtissue is detected, the model is relatively simple, operation is rapid, epithelial boundary detection has obvious advantages in detection precision, meanwhile, the method automatically learns effective features and expressions, the complex manual feature selection process is avoided, and the actual application requirements can be met.

Description

technical field [0001] The present invention relates to a method for annotating pathological images of esophageal cancer, and more specifically, to a method for annotating pathological images of esophageal cancer, which can convert rough contours marked by experts into precise contours. Background technique [0002] Esophageal cancer is a common malignant tumor in life, which seriously affects human health. At present, it is more and more difficult to rely on experts to screen and diagnose esophageal cancer pathological slides as the number of patients increases. The combination of medical imaging and artificial intelligence is a relatively new branch and industrial hotspot in the field of digital medicine. The application of artificial intelligence in the medical field has become a trend. The application of big data-driven artificial intelligence to build a computer-aided diagnosis system for esophageal cancer and relieve the work pressure and intensity of doctors is the d...

AI Technical Summary

Problems solved by technology

The interpretation of medical images requires the accumulation of professional experience for a long time, and the training cycle of pathologists is relatively long, and esophageal cancer lesions occur in the basal layer of esophageal epithelial tissue, close to the junction of epithelial tissue and mesenchymal tissue, so pathologists are required to strictly Defining the contours of epithelial tissue with fine lines is labor-intensive and a huge waste of medical resources

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