Coronary artery CTA epicardial fat automatic extraction and quantification method based on artificial intelligence

Through improved U-Net architecture and multimodal image preprocessing technology, the efficiency and accuracy of automatic extraction and quantitative analysis of epicardial fats are solved, and the automated identification and quantification of epicardial fats are realized, which is suitable for cardiovascular disease diagnosis and treatment planning.

CN120451041APending Publication Date: 2025-08-08YIZHIYUAN HEALTH TECHNOLOGY (HANGZHOU) CO LTD
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Patent Information

Application Number
CN202510362243.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-26
Publication Date
2025-08-08

AI Technical Summary

Technical Problem

The prior art is difficult to efficiently and accurately automatically extract and quantitatively analyze epicardial fat. The traditional methods are time-consuming and affected by the operator's subjectivity. The deep learning model has noise and artifacts in the recognition and segmentation of epicardial fats, and the generalization ability is insufficient.

Method used

Using the improved U-Net architecture combined with multimodal image preprocessing, deep learning models and feature fusion strategies, special loss functions and optimization algorithms are designed to realize automatic extraction and quantitative analysis of epicardial fat through nonlinear filtering, contrast enhancement, and image artifact removal.

Benefits of technology

It significantly improves the efficiency and accuracy of automated processing of epicardial fat, provides efficient and reliable quantitative data support for epicardial fat, adapts to a variety of clinical scenarios, and improves the accuracy and efficiency of medical imaging analysis.

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Abstract

The invention discloses a coronary artery CTA epicardial fat automatic extraction and quantification method based on artificial intelligence, and aims to improve the accuracy and efficiency of fat extraction. The method comprises the following steps: preprocessing a coronary artery CTA image to enhance data quality; a segmentation model based on U-Net is constructed, and accurate segmentation of the heart structure is achieved; the epicardial adipose tissues in the segmented regions are automatically extracted in combination with a set CT value threshold value; quantifying the fat volume by adopting a voxel counting mode, and analyzing the density of the fat volume; a three-dimensional visual model is further generated, and the spatial relation between fat distribution and the heart structure is displayed. According to the method, the effect is verified on multiple data sets, it is shown that the method is superior to an existing method in the aspects of fat extraction precision, volume and density evaluation and model robustness, and the method has good application prospects.
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