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Deep learning image registration method and device, electronic equipment and storage medium

A technology of deep learning and electronic equipment, applied in the field of image processing, can solve problems such as slow registration speed

Pending Publication Date: 2021-02-09
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing registration adopts iterative optimization method, and the registration speed is slow

Method used

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  • Deep learning image registration method and device, electronic equipment and storage medium
  • Deep learning image registration method and device, electronic equipment and storage medium
  • Deep learning image registration method and device, electronic equipment and storage medium

Examples

Experimental program
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Embodiment 1

[0043] figure 1 It is a schematic flowchart of a deep learning image registration method provided by an embodiment of the present invention. The deep learning image registration method provided by this embodiment is applicable to a deep neural network, and the method can be executed by an electronic device. Specifically Specifically, the registration method of a deep learning image provided in this embodiment includes the following steps:

[0044] Step 100, acquire the deformation fields of the first fixed image and the moving image through a first algorithm.

[0045]In this embodiment, this embodiment is mainly applicable to the field of nuclear magnetic resonance imaging, specifically, nuclear magnetic resonance imaging (Nuclear Magnetic Resonance Imaging, NMRI for short), also known as spin imaging (spin imaging), also known as magnetic resonance imaging (Magnetic resonance imaging). Resonance Imaging, referred to as MRI), is to use the principle of nuclear magnetic resona...

Embodiment 2

[0060] figure 2 It is a method flowchart of a deep learning image registration method provided in Embodiment 2 of the present invention. The deep learning image registration method provided in this embodiment is suitable for deep neural networks. This embodiment is based on Embodiment 1 Based on the expansion, specifically, the following steps are included:

[0061] Step 200, acquire the deformation fields of the first fixed image and the moving image through the first algorithm.

[0062] Step 210, acquire a second fixed image according to the deformation field and the moving image.

[0063] Step 220, taking the second fixed image and moving image as input.

[0064] Step 230 , using the deformation field as an output to train an untrained registration network to obtain a trained registration network.

[0065] In this embodiment, the deep neural network is a registration network, and an untrained registration network is pre-trained through various parameter sets to obtain a...

Embodiment 3

[0082] The deep learning image registration device of the embodiment of the present invention can implement the deep learning image registration method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method. image 3 It is a schematic structural diagram of a deep learning image registration device 300 in an embodiment of the present invention. refer to image 3 , the deep learning image registration device 300 provided by the embodiment of the present invention may specifically include:

[0083] An acquisition module 300, configured to acquire the deformation fields of the first fixed image and the moving image through a first algorithm;

[0084] A generating module 310, configured to acquire a second fixed image according to the deformation field and the moving image;

[0085]The output module 320 is configured to train the deep neural network according to the moving image, the second fix...

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Abstract

Embodiments of the invention disclose a deep learning image registration method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining deformation fields ofa first fixed image and a moving image through a first algorithm, acquiring a second fixed image according to the deformation field and the moving image, and training a deep neural network accordingto the moving image, the second fixed image and the deformation field to obtain a trained deep neural network. According to the deep learning image registration method provided by the embodiment of the invention, the simulated deformation field is generated by using the generation algorithm and is not artificially labeled, so that the registration network is better trained, and the problems that the registration standard is defined and the registration process is driven by depending on the artificially set registration metric in the prior art are solved. According to the method, the training data volume is greatly improved in the neural network, the registration accuracy is further improved, and the calculation speed is high.

Description

technical field [0001] Embodiments of the present invention relate to image processing technologies, and in particular to a registration method, device, electronic equipment, and storage medium for deep learning images. Background technique [0002] Medical image registration in recent years can be divided into two categories: (1) using deep learning networks to estimate the similarity measure of two images, driving iterative optimization; (2) directly using deep regression networks to predict transformation parameters. The former only uses deep learning for similarity measurement, and still needs traditional registration methods for iterative optimization. It does not give full play to the advantages of deep learning, takes a long time, and is difficult to achieve real-time registration. According to the type of deep learning used, it can be divided into two categories: registration based on supervised learning and registration based on unsupervised learning. The input ima...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/30G06N3/04G06N3/08
CPCG06T7/0012G06T7/30G06N3/04G06N3/08G06T2207/20081G06T2207/10088G06T2207/30004
Inventor 高毅高珊黄俊赵丙帅张赛
Owner SHENZHEN UNIV
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