Coronary Artery Segmentation by Deep Learning Neural Networks on Computed Tomographic Coronary Angiographic Images

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiovascular Disease

Background:

• Coronary artery lumen delineation is crucial for evaluating coronary heart disease but is challenging for small structures.
• Deep learning shows promise in medical imaging, yet segmentation of small objects like coronary arteries remains difficult.

Purpose of the Study:

• To investigate the efficacy of 3D U-net convolutional neural networks for coronary artery lumen segmentation.
• To evaluate the performance of deep learning models on multiple computed tomography coronary angiography (CTCA) datasets.

Main Methods:

• Adapted CTCA volumes into small patches for 3D U-net analysis.
• Tuned network parameters (kernels, layers, batch size) and applied data transformations to mitigate overfitting.
• Compared subject-specific normalization with patch-based normalization.

Main Results:

• Subject-specific data normalization proved superior to patch-based normalization.
• The proposed deep learning approach demonstrated superior performance over existing methods.
• Performance was quantitatively evaluated using Dice coefficients.

Conclusions:

• 3D U-net convolutional neural networks offer a robust solution for coronary artery lumen segmentation.
• Subject-specific normalization is a key factor in improving deep learning model performance for CTCA analysis.
• This deep learning strategy enhances the accuracy of stenosis localization and grading in coronary heart disease evaluation.