Tissue pathology image cell nucleus segmentation method and system based on polar coordinate representation

Abstract

The invention provides a tissue pathology image cell nucleus segmentation method and system based on polar coordinate representation; wherein the method comprises the steps: extracting the image features of a to-be-segmented image block, and carrying out the modeling based on a polar coordinate system, and obtaining the classification data, central point coordinates and ray length of the image features; according to the classification data, the center point coordinates and the ray length, carrying out cell nucleus segmentation of the to-be-segmented image block to acquire the image block containing the center position coordinates and the edges of each cell nucleus. On the aspect of the processing effect, the invention provides the polar coordinate representation-based automatic cell nucleus segmentation method of the histopathological image for the first time. The method based on polar coordinates is very suitable for segmentation of circular objects; therefore, the pathological imagecell nucleus can be automatically segmented, the cell nucleus center and the ray length pointing to the target edge with the cell nucleus center as the starting point are obtained, and positioning andfine segmentation of the cell nucleus are achieved.

Description

technical field [0001] The present disclosure relates to the field of image processing, and in particular to a method and system for automatically segmenting cell nuclei based on histopathological images represented by polar coordinates. Background technique [0002] The precise positioning and segmentation of cell nuclei is an important step in pathological image analysis, which is used to quantitatively analyze the characteristics of tissues in digital pathological images, so as to realize computer-aided diagnosis. In recent years, artificial intelligence-based methods have shown great potential in pathological image analysis tasks. [0003] Many methods have been proposed for the segmentation of cells and nuclei in histopathological images, such as threshold-based image segmentation methods, edge-based image segmentation methods, and region-growing image segmentation methods. In addition, cell nucleus segmentation methods based on specific theories have also been proposed...

AI Technical Summary

Problems solved by technology

However, the automatic segmentation of cell nuclei in pathological images usually has limitations such as low efficiency and poor results, and it is difficult to achieve fast instance segmentation of cell nuclei in pathological images.

Image target instance segmentation methods based on artificial intelligence, such as Mask-RCNN, can better solve the problem of cell nucleus segmentation in pathological images, but there are limitations such as complex model structure, long training time, and high hardware requirements.

[0004] In summary, there is still a lack of effective solutions to the problem of automatic detection of cell nuclei in histopathological images in existing technologies

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