Immunohistochemical cell image cell nucleus segmentation and counting method and system

Abstract

The invention provides an immunohistochemical cell image cell nucleus segmentation and counting method, which comprises the following steps: acquiring a pathological cell image through a preset input data source; determining a display image on an image display panel according to the pathological cell image and an input data source; determining a cell nucleus segmentation area on the display image through graph drawing; performing color correction and noise reduction on the cell nucleus segmentation region, and determining an initial cell image; processing the initial cell image, obtaining a negative area mask, a positive area mask and a cell nucleus contour, classifying cell nucleuses detected in the initial cell image according to the cell nucleus contour, the negative area mask and the positive area mask, and determining classified cell nucleuses; and counting the classified cell nucleuses, determining a diagnosis index according to the classified cell nucleuses, and optimizing a display result according to the diagnosis index.

Description

technical field [0001] The present invention relates to the technical field of cell nucleus segmentation and counting methods and systems for immunohistochemical cell images, in particular to a cell nucleus segmentation and counting method and system for immunohistochemical cell images. Background technique [0002] At present, pathological diagnosis is the most reliable method of tumor examination and is called the "gold standard". Cancerous nuclei are significantly different from normal nuclei in size and shape, and can be visualized by specific staining. Currently, pathological diagnosis is mostly done through observation under a microscope, which requires very high expertise, takes a lot of time, and produces subjective differences. Computer-aided diagnosis can provide high-speed, repeatable medical image analysis, and the results are more objective and accurate. Nucleus detection and segmentation is a critical first step in medical image analysis. However, due to the ...

AI Technical Summary

Problems solved by technology

Nucleus detection and segmentation is the key first step in medical image analysis. However, due to the existence of uneven staining, irregular cell shape, adhesion between nuclei, and impurity noise, accurate cell nucleus segmentation and counting still face many challenges. It is a big challenge. The existing cell image segmentation technology mainly adopts deep learning algorithm, which requires a large amount of training data. However, it is extremely difficult to obtain labeled samples of immunohistochemical cell images, and the training data is insufficient, resulting in insufficient accuracy.

At the same time, the deep learning algorithm has a large amount of calculation and requires high computer resources.

However, the segmentation method based on non-deep learning algorithm needs to regulate a large number of parameters, and the selection of parameters is more difficult.

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