Three-dimensional CNV growth prediction method and device and a quantitative analysis method

Abstract

The invention discloses a three-dimensional CNV growth prediction method and device and a quantitative analysis method, and the method comprises the steps: sending an original OCT sequence into a three-dimensional segmentation model, and segmenting an original CNV region; selecting a registration reference, sequentially pairing the registration reference with the original OCT sequence, sending theregistration reference into a three-dimensional registration model, and obtaining a registration parameter sequence; sequentially carrying out image transformation on the original OCT sequence by using the registration parameter sequence to obtain a registered OCT sequence; and sending the registered OCT sequence into a three-dimensional CNV growth prediction model to obtain a three-dimensional CNV prediction area. The three-dimensional CNV region can be accurately segmented, the CNV region can be accurately predicted, and the CNV growth region can be quantitatively analyzed and predicted.

Description

technical field [0001] The invention relates to a three-dimensional choroidal neovascularization growth prediction method, device and quantitative analysis method, belonging to the technical field of image processing and analysis. Background technique [0002] Choroidal neovascularization (Choroidal Neovascularization, CNV) refers to the proliferation of blood vessels from the choroidal capillaries, expanding through the breach of Bruch's membrane, between Bruch's membrane and retinal pigment epithelium, or between the neural retina and retinal pigment epithelium, or in the retina Proliferation between the pigment epithelium and the choroid forms. Many diseases involving the RPE-Bruch membrane-choriocapillary complex can lead to the formation of CNV, also known as subretinal neovascularization, which is more common in the macula. CNV diseases impair central vision. [0003] The main method for checking CNV disease is to use Optical Coherence Tomography (OCT). Inspectors usua...

AI Technical Summary

Problems solved by technology

[0004] At present, the existing macular-centered OCT image analysis methods, especially some CNV region segmentation methods of OCT, mainly rely on manual identification and prediction based on experience. OCT contains hundreds of slice scans, which limits the inspectors. Correctly check the efficiency of CNV; the distribution of CNV lesions in each slice is not obvious, and the technical level of the inspectors is strictly required. The accuracy of the inspection results is different due to the cognitive level of the operator, resulting in OCT including CNV A series of lesion area identification and segmentation and prediction of slow and inaccurate problems

At the same time, the identification and segmentation of CNV regions in the few existing OCT images at home and abroad and the growth prediction algorithms of CNV can only rely on a single slice as the information source and lack the mining of time series information, and the robustness of the algorithm and Accuracy is not ideal

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