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LASF: a local adaptive segmentation framework for coronary angiogram segments.

Hao Ren^1,2,3, Dongxiao Li⁴, Fengshi Jing^1,5

¹Faculty of Data Science, City University of Macau, Taipa, 999078 Macao Special Administrative Region China.

Health Information Science and Systems

|January 30, 2025

Summary

This summary is machine-generated.

A new Local Adaptive Segmentation Framework (LASF) improves coronary artery disease (CAD) diagnosis by enhancing medical imaging segmentation. This AI tool offers more accurate vessel identification in coronary angiograms for better patient outcomes.

Keywords:

Coronary angiograms Deep learning Medical imaging Vascular image segmentation YOLOv8

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Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Disease Research

Background:

Coronary artery disease (CAD) is a leading global cause of mortality.
Current coronary angiogram segmentation methods face challenges with vessel discontinuity and accuracy.
Accurate segmentation is crucial for effective CAD diagnosis and treatment planning.

Purpose of the Study:

To develop an advanced segmentation framework for coronary angiograms.
To improve the precision and continuity of vascular segmentation in medical images.
To enhance the diagnostic capabilities for coronary artery disease.

Main Methods:

Developed the Local Adaptive Segmentation Framework (LASF) by enhancing the YOLOv8 architecture.
Integrated dilation and erosion algorithms into the YOLOv8 model for improved segmentation.
Enriched the ARCADE dataset with detailed annotations of proximal and distal vascular segments.
Performed comparative analyses against established models like UNet and DeepLabV3Plus.

Main Results:

LASF demonstrated superior performance compared to UNet and DeepLabV3Plus.
Achieved higher precision, recall, and F1-scores in vascular segmentation tasks.
The enhanced ARCADE dataset improved the robustness of segmentation models.
LASF effectively addressed issues of vessel discontinuity and segmentation inaccuracies.

Conclusions:

LASF offers a significant advancement in segmenting vascular images from coronary angiograms.
The framework provides more reliable and accurate segmentation critical for clinical applications.
LASF has the potential to improve the clinical management of CAD, enhancing diagnostic accuracy and patient outcomes.