TQGDNet: Coronary artery calcium deposit detection on computed tomography

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Cardiology

Background:

• Coronary artery disease (CAD) is a major cause of mortality.
• Coronary artery calcium (CAC) scoring via CT images aids in assessing cardiovascular risk in asymptomatic individuals.
• Current automated CAC scoring methods using convolutional neural networks (CNNs) struggle with integrating features from lower network layers, crucial for detecting small calcium deposits.

Purpose of the Study:

• To develop a new CNN model for enhanced CAC scoring, specifically designed to capture features from small regions and low-contrast areas in CT images.
• To improve the integration of features across multiple CNN layers, particularly focusing on lower layers vital for detecting subtle calcifications.

Main Methods:

• Proposed a novel CNN architecture incorporating a low-contrast detection module (ThrConvs) and two fusion modules: Queen-fusion (Qf) for cross-scale feature fusion and a lower-layer Gather-and-Distribute (GD) module.
• The ThrConvs module uses three convolution blocks for low-contrast object detection.
• The fusion modules connect neurons across adjacent CNN levels to capture comprehensive features of small calcium deposits and their surroundings.

Main Results:

• The proposed model demonstrated superior performance on the public OrCaScore dataset (269 calcium deposits).
• Achieved a 2.3-3.6% improvement in mean Pixel Accuracy (mPA) on both the private Concord and public OrCaScore datasets compared to existing methods.
• Outperformed previous state-of-the-art detection methods in CAC scoring.

Conclusions:

• The novel CNN model effectively captures features from small regions and low-contrast areas, leading to improved CAC scoring accuracy.
• The integration of specialized modules (ThrConvs, Qf, GD) enhances the model's ability to detect subtle coronary artery calcifications.
• This approach offers a significant advancement in automated cardiovascular risk assessment using CT imaging.