Embryonic tissue segmentation method based on generative adversarial network

Abstract

The invention relates to an embryonic tissue segmentation method based on a generative adversarial network, and belongs to the technical field of medical image processing. The method comprises the steps of step 1, performing tissue segmentation mask mapping on an embryonic tissue slice image through a U-net network; step 2, making a segmentation network training set; step 3, configuring parametersrequired by network training to obtain a set network; step 4, training the set organization quality identification network by using the manufactured segmentation network training set; step 5, fixingparameters of the organization quality identification network, and training the set U-net network by using the manufactured segmentation network training set in combination with the organization quality identification network; and step 6, taking the embryonic tissue slice image without the marked segmentation result as input, and generating a corresponding mask image. The network relied on by thesegmentation method uses a classification model to supplement loss during training and segmentation, fully utilizes the information of the cell growth state, and improves the accuracy of the segmentation network in the field of embryo tissue segmentation.

Description

technical field [0001] The invention relates to an embryo tissue segmentation method based on a generative confrontation network, which belongs to the technical field of medical image processing. Background technique [0002] With the rapid development of digital imaging technology, medical imaging is gradually widely used in clinical detection and treatment. With the help of medical imaging, doctors can accurately diagnose and locate the cause in terms of judging the pathological cause, intervening during treatment, and checking the effect after treatment. Such intelligent diagnosis shortens the diagnosis time and improves the accuracy of diagnosis. The observation and classification of medical images can be roughly divided into three processes. At first, experienced doctors rely on experienced doctors to observe the images, which is highly subjective and time-consuming; after adding computer technology, image recognition can be done semi-automatically, that is, with the pa...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to propose a method for embryonic tissue segmentation based on generative adversarial networks for the lack of data samples in the training process of the existing medical assistance system, which results in the use of traditional machine learning methods for training. Neural Network Method for Medical Assisted Diagnosis and Tissue Segmentation Recognition

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