Tissue segmentation method for colorectal panoramic digital pathology images based on deep network

Abstract

The invention discloses a method for tissue segmentation of colorectal panoramic digital pathological images based on a deep network, comprising the following steps: (1) acquiring colorectal panoramic digital pathological images: (2) segmenting the colorectal panoramic digital images; (3) training Establishment of sample images; (4) Extract different types of tissue depth features; (5) Use the classifier and the extracted tissue depth features to discriminate the types of tissues in the segmented image; Tissue classification of the entire picture; (7) Stitching the images together according to the block coordinates; the present invention segments the colorectal panoramic digital pathology image, and uses the sliding window and the trained model to mark the tissue types in sequence for all the segmented images, At the same time, the classifier and the extracted tissue depth features are used to classify the tissue, and the image classification result is obtained, which is accurate and fast.

Description

technical field [0001] The invention discloses a tissue segmentation method for colorectal panorama digital pathological images based on a deep network, belonging to the field of medical image processing. Background technique [0002] At present, the analysis of pathological images is mainly evaluated by pathologists. However, the manual analysis method is extremely time-consuming and involves the subjective judgment of doctors. There are large differences between doctors with different experiences, which will lead to inappropriate treatment or overtreatment. In poorer and backward areas, many people died due to missed treatment due to lack of good doctors and medical equipment. [0003] For pathological tissue images, since they contain a lot of valuable information, different histopathological images can be classified by using some characteristics of the pathological image itself. Histopathological images are highly complex and have many targets. Existing research work o...

AI Technical Summary

Problems solved by technology

Histopathological images are highly complex and have many targets. Existing research work on colorectal digital pathology images is very little. Generally, only some types of tissue areas in the images are detected, and the images are processed one-sidedly, and the processing results are not accurate. precise

[0004] Existing research on colorectal panoramic digital pathological images has not yet done research on colorectal panoramic digital pathological images, generally only for local area segmentation, for example, Multi-class texture analysis in colorectal cancer histology published in Science report in 2016 is Segmentation of various tissues on a small range of colorectal pathological pictures, the segmentation is rough, the accuracy is poor, and the error rate is high. Only cells or some types of tissue areas in the picture are detected, and the scope of application is small.

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