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Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.

Kavita Chachadi¹, S R Nirmala², Pavan G Netrakar²

¹KLE Technological University, Hubballi, Karnataka, India. kavita.chachadi@kletech.ac.in.

Cardiovascular Engineering and Technology

|February 20, 2025

Summary

This study modified a 2D deep learning model into a 3D Pyramid Scene Parsing Neural Network (PSPNet) for segmenting coronary arteries in 3D Coronary Computed Tomography Angiography (CCTA) images, achieving promising results for disease diagnosis.

Keywords:

3D CCTA Images 3D PSPNet Coronary arteries Global process Patch based method

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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 death.
Accurate coronary artery segmentation is crucial for diagnosing CAD, including stenosis and plaque analysis.
Deep learning (DL) shows promise in medical image analysis, but 2D models have limitations for 3D data.

Purpose of the Study:

To adapt the 2D Pyramid Scene Parsing Neural Network (PSPNet) into a 3D model for segmenting coronary arteries.
To evaluate the performance of the proposed 3D PSPNet using both Global and Patch-based processing methods.
To assess the potential of 3D PSPNet for improving the analysis of 3D Coronary Computed Tomography Angiography (CCTA) images.

Main Methods:

Modification of the 2D PSPNet architecture to a 3D version.
Application of the 3D PSPNet for semantic segmentation of coronary arteries in 3D CCTA datasets.
Comparative evaluation of Global processing versus Patch-based processing strategies for segmentation.
Utilizing the ImageCAS dataset for experimental validation.

Main Results:

The 3D PSPNet achieved a Dice Similarity Coefficient (DSC) of 0.76 using the Global processing method.
The Patch-based processing method yielded a DSC of 0.73.
These results demonstrate the feasibility of the proposed 3D PSPNet for coronary artery segmentation.

Conclusions:

The developed 3D PSPNet effectively segments coronary arteries from 3D CCTA images.
The Global processing approach showed slightly superior performance compared to Patch-based processing in this study.
This work contributes a novel deep learning approach for enhancing CAD diagnosis through improved image segmentation.