Abnormal cell automatic labeling method and device, electronic equipment and storage medium

Abstract

The invention relates to an image processing technology, is applied to the field of smart medical treatment, and discloses an abnormal cell automatic labeling method. The method comprises: performingGaussian convolution smoothing processing on a cell pathology picture to obtain an image pyramid; performing detection fitting on the image pyramid through a Hough transform circle detection method toobtain a low-power fitting region of interest; mapping the low-power fitting region of interest to the acquired cell pathology picture to generate a fitting high-power image; segmenting a region of interest in the fitting high-power image to generate a segmented high-power image; labeling and cutting the segmented high-power image through an adaptive threshold segmentation algorithm to obtain anabnormal cell labeling set; and mapping the abnormal cell labeling set to the cell pathology picture to obtain an abnormal cell labeling picture. The accuracy of abnormal cell labeling is improved, and the calculation and storage pressure is reduced. In addition, the invention also relates to a blockchain technology, and the abnormal cell label set can be stored in a blockchain.

Description

technical field [0001] The invention relates to image processing technology, which is applied in the field of smart medical treatment, and in particular to a method, device, electronic equipment and readable storage medium for automatic labeling of abnormal cells. Background technique [0002] At present, due to the lack of pathologists and cytological testing equipment, various artificial intelligence-assisted screening equipment systems are gradually appearing. There are also a large number of methods on the market that use deep learning neural networks for feature extraction and training to detect abnormal cells. However, existing The method still requires a large number of pathologists to label the pathological data, which increases the labor intensity of the pathologists. At the same time, because the detection method of abnormal cells in the neural network requires a large amount of pathological data, it is challenging for the computing pressure and storage performance ...

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Problems solved by technology

[0002] At present, due to the lack of pathologists and cytological testing equipment, various artificial intelligence-assisted screening equipment systems are gradually appearing. There are also a large number of methods on the market that use deep learning neural networks for feature extraction and training to detect abnormal cells. However, existing The method still requires a large number of pathologists to label the pathological data, w

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