Chromosome cutting data processing method and system and storage medium

Abstract

The invention discloses a chromosome cleavage data processing method and system and a storage medium. The method comprises the following steps: acquiring a chromosome original image; classifying the chromosome original images to obtain a single chromosome image as a first image and a chromosome cluster image; carrying out instance segmentation on the chromosome cluster image to obtain a mask of each chromosome in the chromosome cluster image; obtaining a pixel value corresponding to a single chromosome from the chromosome cluster image through the mask of each chromosome; generating a single chromosome image of the chromosome cluster image as a second image according to the pixel value corresponding to each chromosome; and fusing the first image and the second image to obtain a chromosomearrangement diagram composed of all single chromosomes. According to the method, chromosome images with chromosome intersection or overlapping can be accurately segmented, and the accuracy of chromosome classification and karyotype analysis by doctors is improved. The method can be applied to the field of image processing.

Description

technical field [0001] The invention relates to the field of image processing, in particular to a method, system and storage medium for processing chromosome cutting data. Background technique [0002] Chromosome cutting is an important stage in the process of chromosome karyotype analysis. The result of chromosome cutting will directly affect the accuracy of doctors' chromosome classification and karyotype analysis. In the prior art, the karyotype analysis is mainly completed through a computer-aided system, but none of them can realize a fully automated process, and a large amount of manual intervention is required. Under good imaging conditions, existing automatic classification systems can achieve a certain degree of accuracy. However, if chromosomes are connected, overlapped or deformed in the image, the classification error rate will increase significantly. It can be seen that the effect of directly classifying the original image is not ideal, and the key is to obtain...

AI Technical Summary

Problems solved by technology

Existing technologies mainly use computer-aided systems to complete karyotype analysis, but none of them can achieve a fully automated process and require a lot of manual intervention

Under good imaging conditions, the existing automatic classification system can achieve a certain accuracy, but if the chromosomes are connected, overlapped or deformed in the image, the classification error rate will be significantly increased

It can be seen that the effect of directly classifying the original image is not ideal, and the key is to obtain more accurate analysis results by cutting out a single chromosome

However, most of the existing chromosome image segmentation methods are based on geometric methods. For chromosome images with chromosome crossover or overlap, the segmentation accuracy is greatly reduced.

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