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MDP-net-based vitreous opacity grading screening method and device

A vitreous opacity, mdp-net technology, applied in the field of image processing and deep learning, can solve the problems of low accuracy, doctors can only make intuitive judgments, consume doctors' energy, etc., and achieve fast results.

Pending Publication Date: 2022-03-04
FOSHAN UNIVERSITY
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Problems solved by technology

This method has the problems of strong dependence on doctors' clinical experience and low diagnostic efficiency, and it is difficult to obtain accurate and reliable screening results in a short time. When the number of images is large, it will consume more energy for doctors and will cost more time, and doctors can only judge intuitively, with low accuracy

Method used

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  • MDP-net-based vitreous opacity grading screening method and device
  • MDP-net-based vitreous opacity grading screening method and device
  • MDP-net-based vitreous opacity grading screening method and device

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Embodiment Construction

[0027] The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

[0028] refer to Figure 1a , Figure 1b and Figure 1c , MDP-net network construction includes:

[0029] Encoding part (downsampling)

[0030] 1. First run the software python and the input image size is 256*256;

[0031] 2. For the first time, it is input to a standard convolution layer with a number of convolution kernels of 96 and a kernel size of 3*3;

[0032] 3. After that, 5 Denseblock operations were used. The Denseblock block is composed of multiple convolutional layers and Concatenate cascade layers. The specific structure is shown in the figure.

[0033] 4. Also took a total of 5 downsampling operations, interspersed between each block using LeakyRelu ...

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Abstract

According to the MDP-net-based vitreous opacity grading screening method and device, a crystalline lens, a quasi-trapezoidal vitreous cavity and a turbid focus area in an eye vitreous ultrasonic image can be segmented through deep learning by an eye vitreous ultrasonic image adaptive segmentation model and a severity classification algorithm; according to the method, the vitreous opacity severity of the patient can be quickly and accurately judged and screened after image training by applying the deep learning technology, and the obtained result is quick, objective, accurate and stable.

Description

technical field [0001] The present invention relates to image processing and deep learning technology, in particular to a vitreous opacity grading screening method and device based on MDP-net (full name: Multi-output Dense Pyramid network, named Multi-output Dense Pyramid Network). Background technique [0002] The specific condition of vitreous opacity can be reflected by the shape and size of opacity foci in the vitreous cavity in the images taken by ophthalmic B-ultrasound equipment, but the existing instruments fail to provide the quantitative indicators required for the diagnosis of vitreous opacity. The known grading method for the severity of vitreous opacity is: doctors evaluate and analyze vitreous B-ultrasound images based on clinical experience and professional knowledge. This method has the problems of strong dependence on doctors' clinical experience and low diagnostic efficiency, and it is difficult to obtain accurate and reliable screening results in a short t...

Claims

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06T7/136G06T7/155
CPCG06T7/0012G06T7/11G06T7/136G06T7/155G06T2207/20016G06T2207/30041
Inventor 刘明迪覃楚渝杜倩宜陈子源杨凌风劳俊星郭学东王陆权熊红莲曾亚光韩定安
Owner FOSHAN UNIVERSITY
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