Image multi-mode registration method based on asynchronous deep reinforcement learning

A reinforcement learning, multi-modal technology, applied in the field of image processing, to achieve the effect of stable image registration and improved performance
CN110211165AActive Publication Date: 2019-09-06CHENGDU UNIV OF INFORMATION TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
CHENGDU UNIV OF INFORMATION TECH
Publication Date
2019-09-06

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Abstract

The invention discloses an image multi-mode registration method based on asynchronous deep reinforcement learning. The registration method comprises the following steps: inputting two pictures of different modes (such as CT and MRII) into a neural network in a stacking manner for processing, and outputting current state value information and probability distribution information of strategy actions; moving the dynamic image in the environment according to the probability distribution information and returning a reward value; judging whether the current network state value information reaches athreshold; and sampling the current image registration and outputting a final result. The method is based on reinforcement learning (A3C algorithm). According to the technical scheme, a user-defined reward function is provided, a cyclic convolution structure is added to make full use of space-time information, Monte Carlo is adopted for image registration, the registration performance is improved,and compared with an existing registration method, the registration result is closer to a standard registration image, and the registration of images with large differences is more stable.
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Description

technical field

[0001] The invention relates to the technical field of image processing, in particular to an image multimodal registration method based on asynchronous deep reinforcement learning. Background technique

[0002] Multimodal medical image processing is a research hotspot in current image processing, which is of great significance for clinical diagnosis and treatment. Images of different modalities provide different information of the patient, anatomical images (such as CT, MRII) provide information on the anatomical structure of the human body, and functional images (such as SPECT, PET) provide functional information on the distribution of radioactive concentrations in the human body. The information needs to be synthesized to obtain a more comprehensive fusion image. To obtain useful fusion images, images of different modalities need to be registered.

[0003] Medical image registration is to find a certain spatial transformation, so that the corresponding po...

Claims

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