Multispectral fundus image analysis method and system based on adversarial learning

A fundus image and analysis method technology, applied in image analysis, image enhancement, image data processing and other directions, can solve the problems of pixel misalignment, no joint analysis of multispectral fundus images, etc., to improve accuracy, realize automatic image analysis, improve The effect of precision

Active Publication Date: 2021-03-02
SHANDONG NORMAL UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

The second challenge is the misalignment of pixels due to the constant movement of the eyes
Complex eye movements during imaging create great challenges in analyzing MSI images
[0007] Multispectral fundus image registration is usually based on the alignment of images based on the retinal vascular structure, and many ophthalmic diseases can be reflected on retinal blood vessels. The extraction of retinal blood vessels is a key technology for analyzing fundus images. Therefore, the segmentation of retinal blood vessels is a multispectral A key step in fundus image analysis, but currently there is no method for joint analysis of multispectral fundus image alignment and blood vessel segmentation

Method used

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  • Multispectral fundus image analysis method and system based on adversarial learning
  • Multispectral fundus image analysis method and system based on adversarial learning
  • Multispectral fundus image analysis method and system based on adversarial learning

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Experimental program
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Embodiment 1

[0042] In this embodiment, a multispectral fundus image analysis method based on adversarial learning is disclosed, such as Figure 1-Figure 8 shown, including:

[0043] Acquire multispectral fundus images, including images with vascular labels and images without vascular labels;

[0044] Input the multispectral fundus image into the trained fundus image analysis model to obtain the registration result of the multispectral fundus image;

[0045] Among them, the fundus image analysis model includes a fundus image registration model and a retinal vessel segmentation model. During training, the fundus image registration model and the retinal vessel segmentation model are trained separately using images with blood vessel labels; the fundus image registration model and retinal vessel segmentation model The model performs adversarial learning on labeled images without vessels.

[0046] The vessel map corresponding to the multispectral fundus image is obtained through the retinal v...

Embodiment 2

[0123] In this embodiment, a multispectral fundus image analysis system based on adversarial learning is disclosed, including:

[0124] The data acquisition module collects multi-spectral fundus images;

[0125] The data analysis module registers the multispectral fundus image through the fundus image analysis model, wherein the fundus image analysis model includes a fundus image registration model and a retinal blood vessel segmentation model, and obtains the blood vessel map corresponding to the multispectral fundus image through the retinal blood vessel segmentation model , the fundus image registration model registers multispectral fundus images according to the blood vessel map. During training, the fundus image registration model and retinal vessel segmentation model are trained separately using images with blood vessel labels; the fundus image registration model and retinal vessel segmentation model The model performs adversarial learning on labeled images without vesse...

Embodiment 3

[0127] An electronic device is disclosed in this embodiment, including a memory, a processor, and computer instructions stored in the memory and run on the processor. When the computer instructions are executed by the processor, a Steps in a multispectral fundus image analysis method based on adversarial learning.

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Abstract

The invention discloses a multispectral fundus image analysis method and system based on adversarial learning, and the method comprises the steps: obtaining a multispectral fundus image which comprises a blood vessel label image and a blood vessel label-free image; inputting the multispectral fundus image into a trained fundus image analysis model to obtain a registration result of the multispectral fundus image; wherein the eye fundus image analysis model comprises an eye fundus image registration model and a retinal blood vessel segmentation model, and during training, the eye fundus image registration model and the retinal blood vessel segmentation model are independently trained by adopting blood vessel label images respectively; and the fundus image registration model and the retinalvessel segmentation model perform adversarial learning according to the vessel-free label image. The fundus image registration model and the retinal blood vessel segmentation model can perform adversarial learning according to the image without the blood vessel label besides performing independent training through the image with the blood vessel label, so that the precision of image registration and blood vessel segmentation is improved.

Description

technical field [0001] The present disclosure relates to a multispectral fundus image analysis method and system based on adversarial learning. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Effective imaging techniques are key to the diagnosis and successful treatment of ophthalmic diseases. In recent years, various imaging techniques have been developed for fundus imaging, including color fundus photograph (CFP), multispectral imaging (MSI) enhanced depth imaging optical coherence tomography (EDI-OCT), fundus autofluorescence (FAF) , indocyanine green angiography performed fundus fluorescein angiography (ICGA / FFA). Different ophthalmic imaging techniques have shown unique strengths in revealing specific ophthalmic diseases. [0004] Multispectral fundus imaging (MSI) technology is based on light emitting diode (LED) illumination tak...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/33G06T7/10
CPCG06T7/33G06T7/10G06T2207/30041
Inventor 郑元杰隋晓丹姜岩芸贾伟宽赵艳娜牛屹
Owner SHANDONG NORMAL UNIV
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