Cell nucleus segmentation method, system and device and cancer auxiliary analysis system and device based on pathological image

Abstract

The invention discloses a cell nucleus segmentation method, system and device and a cancer auxiliary analysis system and device based on a pathological image, and belongs to the technical field of medical images. The invention aims to solve the problem that the edge segmentation accuracy needs to be improved in the process of segmenting a feature map by a neural network at present. The cell nucleus segmentation method provided by the invention comprises the following steps: aiming at a to-be-detected sample, preparing a slice and dyeing to obtain a slice dyeing image; performing image block segmentation on the section dyeing image; and then performing nucleus segmentation on an image block corresponding to the section dyeing image of the to-be-detected sample by using the nucleus segmentation network model to obtain a nucleus boundary segmentation image. According to the cancer auxiliary analysis system based on the pathological image, a cancer auxiliary analysis module is additionally arranged on the basis of cell nucleus segmentation, cancerous cells are recognized and classified according to the segmentation result of the image segmentation module based on the expert database, and therefore cancer auxiliary analysis is achieved. The method is mainly used for nucleus segmentation and cancer auxiliary analysis.

Description

technical field

[0001] The invention relates to a cell nucleus segmentation method, system and cancer auxiliary analysis system, belonging to the technical field of medical imaging. Background technique

[0002] With the development and maturity of deep learning technology, deep learning technology has become the mainstream technology or research direction in many application fields, and has achieved very good recognition and detection results in many fields.

[0003] At present, many researchers and scholars use deep learning technology for the segmentation and identification of cancer cells, so as to assist doctors in diagnosing and analyzing cancer and reduce the workload of doctors. Some of the existing methods of using deep learning technology to identify cancer cells start with improving the staining effect, supplemented by some conventional neural networks for identification, and some start with cell morphology and improve the neural network to improve the identificat...

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