Confrontation-based bidirectional consistency constraint medical image registration algorithm

Abstract

The invention discloses a confrontation-based bidirectional consistency constraint medical image registration algorithm. The algorithm comprises the following steps: acquiring medical images of CT and MRI modals, and carrying out standardized preprocessing on the medical images; constructing a deep learning neural network, including construction of a generator and construction of a discriminator; randomly selecting a training sample set, and performing adversarial training on the deep learning neural network constructed in the step S2; and inputting the to-be-registered medical image pair into the trained deep learning neural network, and outputting the registered image pair, so that the problems of few medical image registration samples, difficulty in multi-modal registration and difficulty in similarity measurement selection at present are solved, and a high-precision registration effect is achieved. According to the medical image registration method, the medical image registration method and the medical image registration system, for example, registration of a preoperative CT image and an MRI image of the same patient, algorithm training can be completed without providing a large number of sample sets and sample labels, meanwhile, operation stability is guaranteed, and a high-precision medical image registration image pair can be automatically obtained.

Description

technical field [0001] The invention relates to the field of image processing algorithms, in particular to a confrontation-based two-way consistency constraint medical image registration algorithm. Background technique [0002] For image-guided radiotherapy, radiosurgery, minimally invasive surgery, endoscopy, and interventional radiotherapy, medical image registration is one of the key technologies for auxiliary diagnosis and treatment, so it has a wide range of applications, and intelligent registration can Greatly improve the registration efficiency. On the other hand, with the advancement of hierarchical diagnosis and treatment, the level of doctors is uneven, which is more prominent in grassroots hospitals. Therefore, there is a demand for artificial intelligence-assisted diagnosis and treatment, and intelligent registration technology is needed. [0003] The existing technical solutions include the following aspects: [0004] Traditional deformable registration algor...

AI Technical Summary

Problems solved by technology

[0005] Disadvantages: Since the traditional registration method is a high-dimensional mathematical optimization problem, the calculation cost is high and the time consumption is large

[0008] Disadvantages: It still involves high-dimensional optimization and parameter tuning, and the calculation overhead is large

[0010] Disadvantages: The problem of medical image registration with supervised learning is a sample problem and an annotation problem

Especially when it is necessary to evaluate the loss of similarity measures across modalities or sequences, most similarity measures do not work well between different modalities

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