Semi-supervised learning cell segmentation method based on a generative adversarial network

Abstract

The invention discloses a semi-supervised learning cell segmentation method based on a generative adversarial network, which comprises the following steps: collecting cell segmentation data, preprocessing and enhancing the data, and dividing the data into a training set and a test set picture. A new adversarial generation network is designed by taking semi-supervised learning as a starting point.Compared with a previous adversarial generation network, the network replaces a generator with a small-parameter full-volume integral cut network and is used for outputting a probability graph to an input picture. For a cell picture without a label, a semi-supervised method is used for training a segmentation network, after initial segmentation prediction of an unmarked image is obtained from thesegmentation network, a segmentation prediction probability graph is transmitted through a discrimination network, and a confidence graph is obtained. The confidence map is used as a supervision signal, a self-learning mechanism is used to train a segmentation network, and the confidence map represents the quality of prediction segmentation. Through the convolutional neural network designed by theinvention, the cell segmentation accuracy is improved.

Description

technical field [0001] The invention relates to the field of biomedical image processing and computer application, in particular to a cell segmentation method based on semi-supervised learning of confrontation generation network. Background technique [0002] Cell segmentation is the most important step in the study of cell movement and cell morphology. Segmenting precise cells from medical images is currently a challenging topic. With the rapid development of Internet technology, traditional research methods have become less applicable. The traditional method of using human eyes under a microscope to stain, classify, and count cells requires a lot of cumbersome human operations, and its reusability relatively low. [0003] In the research of machine vision, segmentation methods based on deep learning can effectively solve some object segmentation problems. But the most important prerequisite is that a large number of manually labeled medical images are required, which req...

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is to provide a method to solve the problem of relying on a large amount of manual labeling data in the cell segmentation method based on deep learning through the method of confronting the generative network, and to improve the accuracy of cell segmentation

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