Cervical cell image segmentation method based on antagonistic generation network

Abstract

The invention discloses a cervical cell image segmentation method based on an antagonistic generation network, comprising the following steps: a cell image is coarsely segmented, wherein for the cellimage coarse segmentation, a threshold method and a watershed algorithm are used for coarse segmentation of an original image to form guiding factors, and the original image is cut into small images;a virtual body segmentation image is generated, wherein the generated virtual body segmentation image is generated by using an antagonistic generation network designed in combination with a self-encoder, taking a clipped small image as an input, and using the guiding factors to help the neural network to locate a region of interest; a solid cell image is extracted, wherein the solid cell image extraction refers to that a real cell image is extracted from the clipped small image according to the virtual body segmentation image. The cervical cell image segmentation method based on the antagonistic generation network provided by the invention is the first time to use the antagonistic generation network to solve such problems, provides a novel automatic cell image segmentation method, and simultaneously solves the component loss in the traditional overlapped cell segmentation method.

Description

technical field [0001] The invention relates to the technical field of medical image processing, in particular to a cervical cell image segmentation method based on an adversarial generation network. Background technique [0002] Cervical cancer is one of the most common gynecological malignancies. Although cervical cancer has a high morbidity and mortality rate, early detection and treatment can effectively reduce the risk of death. Therefore, accurate and efficient early detection of cervical cancer cells can help save more women's lives. In the past 20 years, most of the detection methods of cervical cancer cells generally adopt the strategy of firstly separating single cells from the background, and then identifying them one by one. In this process, the quality of cervical cell image segmentation also has a very important impact on the accuracy of the final detection results. An ideal cell image segmentation result will not only reduce the complexity of the subsequent ...

AI Technical Summary

Problems solved by technology

The main purpose of the rough segmentation of the cell image in the present invention is to cut the large-size cell image into a small-size image containing only a complete single cell as much as possible to meet the next use. On the one hand, it is to solve the problem of convolutional neural network processing large-size image calculation On the other hand, the overlapping cell segmentation is also converted into a single cell segmentation problem, so that the pixel assignment in the overlapping area is no longer a one-to-one mapping, but a one-to-many mapping, so as to avoid overlapping cell segmentation. incomplete

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