Cardiac image registration system and method based on semi-supervised loop GAN

Abstract

A heart image registration system and method based on semi-supervised loop generation confrontation network GAN. Generate a confrontational network GAN to obtain the corresponding modality block image; register the block image to obtain the registration transformation matrix and the registered image; transfer the registration transformation matrix of the block image with the largest normalized mutual information value to the entire image image to obtain the registration image of the entire image; fusing the registration image and the reference image to display the fused image. The present invention utilizes the semi-supervised cycle to generate an adversarial network GAN, reduces the modal difference of cardiac images, solves the problem of image registration with large deformation, improves the registration efficiency of cardiac images, and improves the registration of cardiac images by using the local information of cardiac images after segmentation. quasi-precision.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a cardiac image registration system and method based on a semi-supervised cyclic GAN (Generative Adversarial Networks) in the technical field of image registration. The invention can be used for registering the images of computed tomography CT (Computed Tomography) obtained before cardiac operation and images of transesophageal echocardiography (TEE) obtained during operation. Background technique [0002] Image registration refers to aligning two images into a common coordinate system so that changes between the two can be monitored. Registration based on feature points is one of the most commonly used image registration methods. Feature points need to be able to be distinguished from adjacent image points. If this condition is not met, it is impossible to uniquely match it with a corresponding point in another image. Therefore, a feature neighborhood should be...

AI Technical Summary

Problems solved by technology

The advantage of this method is to automatically determine the reference image and the region of interest, combined with the rigid body and non-rigid body registration method to achieve the global and local registration of four-dimensional myocardial perfusion MRI images in time series, but the shortcomings of this method are: , this method is to obtain reference images through heartbeat timing operations, and limit the registration image to one heartbeat cycle. When the images to be registered are different heartbeat cycles, the registration accuracy is low

However, the disadvantage of this method is that this method requires manual interaction during image segmentation, which makes the registration process cumbersome and affects the registration efficiency.

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