Retinal vessel segmentation method fusing W-net and conditional generative adversarial network

A technique for generating retinal blood vessels and conditions, applied in biological neural network models, image analysis, image data processing, etc., can solve problems such as over-segmentation, insufficient segmentation of microvessels, low sensitivity, etc., achieve optimal performance, improve parameter utilization, The effect of improving sensitivity

Active Publication Date: 2020-03-27
JIANGXI UNIV OF SCI & TECH
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Problems solved by technology

[0004] The purpose of the present invention is to propose a retinal vessel segmentation method that integrates W-net and conditional generative adversaria...

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  • Retinal vessel segmentation method fusing W-net and conditional generative adversarial network
  • Retinal vessel segmentation method fusing W-net and conditional generative adversarial network
  • Retinal vessel segmentation method fusing W-net and conditional generative adversarial network

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Embodiment Construction

[0041] The present invention expands U-net to W-net, and uses depth-separable convolution and residual modules in W-net to avoid gradient disappearance due to too deep network, introduces SE module, and distributes weights to each channel , so as to ensure that important features are fully learned, avoid learning useless features, and integrate W-net with conditional generation confrontation network, which can make full use of the strong learning ability of W-net for microvascular features and the strong discrimination ability of CGAN for microvascular features. Extract as many microvessels as possible while ensuring the complete extraction of main vessels. The invention has the advantages of high retinal blood vessel segmentation accuracy and low model complexity, can be used as a computer-aided diagnosis system, improves the doctor's diagnosis efficiency, reduces the misdiagnosis rate, and saves precious time of patients.

[0042] Experiment description: The example data com...

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Abstract

The invention relates to application of a deep learning algorithm in the field of medical image analysis, in particular to a retinal vessel segmentation algorithm fusing W-net and a conditional generative adversarial network. According to the method, the problems of low segmentation sensitivity and insufficient micro blood vessel segmentation are well solved, great progress is made in the aspectsof the parameter utilization rate, the information circulation and the feature analysis ability of the network, complete segmentation of the main blood vessel and fine segmentation of the micro bloodvessel are facilitated, the blood vessel intersection is not prone to breakage, and a focus and an optic disc are not prone to being segmented into the blood vessel by mistake. According to the invention, a plurality of network models are fused under the condition of low complexity; the whole segmentation performance on a DRIVE data set is excellent, the sensitivity and accuracy of the method are87.18% and 96.95% respectively, the ROC curve value reaches 98.42%, the method can be used for computer-aided diagnosis in the medical field, and rapid and automatic retinal vessel segmentation is achieved.

Description

technical field [0001] The invention relates to the application of deep learning algorithms in the field of medical image analysis, in particular to a retinal vessel segmentation algorithm that integrates W-net and conditional generation confrontation network. Background technique [0002] Diabetic retinopathy, cardiovascular disease, hypertension, arteriosclerosis and other diseases have different effects on retinal blood vessels, which can be diagnosed by analyzing the characteristics of blood vessels in retinal fundus images such as length, width, angle, curvature and branch form. In order to obtain a more accurate pathological diagnosis, retinal vessels must be accurately segmented from fundus images, and manual segmentation of retinal vessels is a cumbersome, complex and highly professional task, and the segmentation standards are highly subjective. In order to improve the doctor's diagnostic efficiency and reduce the misdiagnosis rate, a computer-aided diagnosis system...

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Application Information

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IPC IPC(8): G06T7/11G06N3/04
CPCG06T7/11G06T2207/20084G06T2207/30101G06N3/048
Inventor 梁礼明蓝智敏吴健盛校棋杨国亮冯新刚
Owner JIANGXI UNIV OF SCI & TECH
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