Cancer WSI segmentation method based on local classification neural network

Abstract

The invention belongs to the technical field of medical image intelligent processing, and particularly relates to a cancer full-view digital pathological section segmentation method based on a local classification neural network, which comprises the following steps of: dividing a pathological section into a plurality of image blocks which have fixed sizes and are not overlapped; sending each imageblock into a classification model based on a convolutional neural network, judging the category of the block through forward propagation, and splicing the classification results of the blocks according to the positions of the blocks on the original image, thereby obtaining a heat map of different tissue distribution in the whole slice; finally, conducting median filtering on the heat map, removing an area with a too small area, so as to obtain a segmentation image and assist a doctor in diagnosis. By means of the method, the areas where different tissues in the whole pathological section arelocated can be rapidly marked, doctors are effectively assisted in diagnosis, and the diagnosis accuracy and efficiency are improved.

Description

technical field [0001] The invention belongs to the technical field of medical image intelligent processing, and in particular relates to a method for segmenting pathological slices, and more specifically, relates to a method for segmenting cancer full-field digital pathological slices based on a local classification neural network. Background technique [0002] With the development of whole slide scanning technology, a large number of tissue slices are scanned as whole-field digital pathological slides (Wholeslide image, WSI), stored in digital form, and widely used in cancer pathological diagnosis. Although professional doctors can complete the diagnosis by analyzing WSI, it is difficult for doctors to pay attention to all the details of WSI due to its huge size. At the same time, there are many factors that affect the prognosis of cancer, and it is difficult for doctors to analyze the relevant information of the patient's prognosis through pathological slides. [0003] B...

AI Technical Summary

Problems solved by technology

Although professional doctors can complete the diagnosis by analyzing WSI, it is difficult for doctors to pay attention to all the details due to the huge size of WSI

At the same time, there are many factors that affect the prognosis of cancer, and it is difficult for doctors to analyze the relevant information of the patient's prognosis through pathological sections.

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