Cross-modal medical image registration method and computer readable storage medium

Abstract

The invention provides a cross-modal medical image registration method and a computer readable storage medium. The method comprises the following steps: constructing a cross-modal medical image registration model of a full-resolution residual registration network; connecting a floating image and a reference image by adopting a full-resolution stream, and sequentially adding residual errors returned by a multi-scale residual error stream parallel to the full-resolution stream to enrich full-resolution feature information of the full-resolution stream; gradually reducing the number of channels through a continuous residual learning module, and estimating a full-resolution deformation field through a 3D convolution module; warping the floating image through a spatial transformation network based on the full-resolution deformation field so as to evaluate the similarity between the warped floating image and the reference image; training a model; and inputting the floating image to be registered and the reference image for registration to obtain multi-modal image registration. The problems of potential information loss and poor local registration quality in full-resolution prediction ofa conventional single-stream encoder decoder structure are solved by adding full-resolution streams.

Description

technical field [0001] The invention relates to the technical field of multi-modal medical image registration, in particular to a cross-modal medical image registration method and a computer-readable storage medium. Background technique [0002] Multimodal medical image registration is the process of aligning a pair of floating images of different modalities with a reference image, with the goal of finding the optimal spatial transformation that aligns regions of interest in the registered image pair. In image-guided therapy (IGT), such as preoperative planning, intervention, and diagnosis, multimodal deformable image registration is key to integrating complementary information contained in different image modalities. In recent years, deep learning-based image registration methods have become more prominent in time-intensive applications due to the large improvement in computational efficiency over traditional iterative registration methods. Most learning-based registration...

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Problems solved by technology

[0005] In order to solve the existing problems, the present invention provides ...

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