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Method, system and equipment for automatic detection of coronary artery lesions based on deep learning

A coronary artery disease and deep learning technology, applied in the field of coronary artery disease automatic detection, can solve the problems of ignoring spatial information, low pixel detection accuracy, and large amount of calculation, so as to shorten the diagnosis and treatment process and improve the lesion detection rate.

Active Publication Date: 2020-11-24
北京红云视界技术有限公司 +1
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  • Summary
  • Abstract
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  • Claims
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AI Technical Summary

Problems solved by technology

Using manual features is time-consuming and labor-intensive, and different features are used for different lesions
However, using a deep network based on the classification task performed pixel by pixel, this method has a considerable amount of calculation, resulting in the inability to detect lesions in medical images in real time, and the accuracy of pixel detection is low because it ignores the spatial information in the image

Method used

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  • Method, system and equipment for automatic detection of coronary artery lesions based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0054] Embodiment 1 of the present invention provides an automatic detection method for coronary artery lesions based on deep learning. The method includes the following steps:

[0055] Step 1. Training steps, including:

[0056] S101. The text information processing module extracts the basic characteristics of the patient from the coronary artery lesion medical records stored in the comprehensive medical database, and uses the C4.5 decision tree algorithm to train a classification decision tree based on the extracted basic characteristics, and the classification decision tree outputs the lesion type information To the Dicom video processing module;

[0057] S102, the Dicom video processing module uses the received lesion type information to filter key frames containing lesions from the cardiac angiography Dicom video corresponding to the lesion type information stored in the medical comprehensive database using the SSN, and outputs the key frames to In the neural network co...

Embodiment 2

[0094] Another aspect of the present invention also provides a deep learning-based automatic detection system 200 for coronary artery lesions, including: a training unit and a testing unit;

[0095] Training units, including:

[0096] The text information processing module is used to extract the basic characteristics of the patient from the coronary artery lesion medical records stored in the medical comprehensive database, and use the C4.5 decision tree algorithm to train a classification decision tree based on the extracted basic characteristics, and the classification decision tree combines the lesion type information Output to the Dicom video processing module;

[0097] The Dicom video processing module is used to filter key frames containing lesions from the cardiac angiography Dicom video corresponding to the lesion type information stored in the medical comprehensive database using the SSN through the received lesion type information, and output the key frames into the...

Embodiment 3

[0110] Such as figure 2 As shown, another aspect of the present invention also provides a deep learning-based automatic detection device 300 for coronary artery lesions, including the system 200 described in the second embodiment.

[0111] The present invention provides an automatic detection device for coronary artery lesions based on deep learning, which applies the object detection technology based on deep learning to the detection of coronary artery lesions. Applying machine learning-based text processing techniques to coronary artery lesion detection. Integrating text processing and image processing technology, the information of multiple modalities is fused for coronary artery lesion detection. Fully automate the process of coronary artery lesion detection, without manual participation in the detection process. The technical solution of the invention can detect the lesion in the coronary arteries of the heart in real time, and provide reference and help to doctors. C...

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Abstract

The present invention provides a method, system and device for automatic detection of coronary artery lesions based on deep learning. The object detection technology based on deep learning is applied to the detection of coronary artery lesions through training steps and testing steps. Applying machine learning-based text processing techniques to coronary artery lesion detection. Integrating text processing and image processing technology, the information of multiple modalities is fused for coronary artery lesion detection. Fully automate the process of coronary artery lesion detection, without manual participation in the detection process. The technical solution of the present invention solves the technical problem that the lesion in the medical image cannot be detected in real time, and the accuracy of pixel detection is low, and the lesion in the coronary artery of the heart can be detected in real time, and can provide reference and help to doctors. Compared with other systems, the invention significantly improves the lesion detection rate and shortens the diagnosis and treatment process.

Description

technical field [0001] The present invention relates to the technical field of digital image target detection, in particular to a method, system and equipment for automatic detection of coronary artery lesions based on deep learning. Background technique [0002] Coronary artery disease is one of the diseases with the highest mortality rate in the world. Digital Silhouette Angiography (DSA) and (CT) are currently the main methods that can diagnose the degree of cardiac arterial disease. Generally, doctors send a catheter into the coronary sinus of the heart, and then release a contrast agent through the catheter to visualize the coronary arteries. After that, the doctor observes according to different body positions and finally confirms the location of the lesion. Due to the delay in the flow of contrast medium and other reasons, it is sometimes difficult to accurately judge the lesion of a blood vessel. Detecting regions of interest or lesions in medical images is a key s...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06N3/08G06N3/04
CPCG06N3/084G06T7/0012G06T2207/30101G06T2207/20221G06T2207/20081G06T2207/10016G06N3/045
Inventor 徐波杜天明周文辉
Owner 北京红云视界技术有限公司
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