AMD lesion OCT image classification segmentation method and system based on bidirectional guide network

Abstract

The invention relates to an AMD lesion OCT image classification segmentation method and system based on a bidirectional guide network, and the method comprises the following steps: obtaining an OCT image, and dividing the OCT image into a training set, a verification set and a test set; constructing a mask complementary convolutional neural network for classification of OCT images; adopting a Grad-CAM algorithm to calculate a class activation graph of the mask complementary convolutional neural network, and obtaining the output of the class activation graph; constructing a class activation graph guided U-shaped segmentation network for segmenting a lesion area in the OCT image; training the network through the training set and the verification set to obtain an optimized mask complementary convolutional neural network and a class activation graph guided U-shaped segmentation network; and substituting the test set into the optimized mask complementary convolutional neural network and the U-shaped segmentation network guided by the class activation graph to realize classification and segmentation of the OCT image. According to the method, OCT images containing glass membrane warts, CNV and normal retinas can be accurately classified, and an accurate segmentation result of a lesion area is generated.

Description

technical field [0001] The invention relates to the technical field of OCT image processing, in particular to a method and system for classifying and segmenting OCT images of AMD lesions based on a bidirectional guidance network. Background technique [0002] Age-related macular degeneration (AMD) is the third leading cause of blindness in the world, seriously endangering the vision health of the elderly. AMD can be divided into dry AMD and wet AMD, and drusen and choroidal neovascularization are the manifestations of these two types, respectively. Optical coherence tomography (OCT) is commonly used to observe drusen and choroidal neovascularization lesions. [0003] Drusen are caused by the reduced ability of the retinal pigment epithelium to transport nutrients and waste materials, resulting in accumulation of waste materials beneath the RPE layer. Appears as small spicule-like protrusions beneath the RPE layer on OCT images. The early glass membrane lesion area is smal...

AI Technical Summary

Problems solved by technology

[0006] For this reason, the technical problem to be solved by the present invention is to overcome the commonly used algorithms such as support vector machine, random forest, convolutional neural network in the prior art and all are used in the classification of AMD, but can't give fine lesion area simultaneously, is unfavorable for Technical limitations of quantitative analysis of lesions

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