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Author Spotlight: Advancing Cardiovascular Imaging - Introducing the Spatially Weighted Calcium Score for Early Disease Detection
Published on: September 22, 2023
Coronary artery segmentation in non-contrast calcium scoring CT images using deep learning
Mariusz Bujny1, Katarzyna Jesionek2, Jakub Nalepa3
1Graylight Imaging, ul. Bojkowska 37a, Gliwice, 44-100, Poland.
Insights
This study introduces an efficient deep learning algorithm for segmenting coronary arteries in non-contrast CT scans. The novel approach significantly improves accuracy, outperforming training data and nearing interrater variability for better cardiac pathology assessment.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiovascular Imaging
Background:
- Accurate coronary artery segmentation is vital for diagnosing heart conditions.
- Non-contrast CT scans offer a less invasive imaging option but present segmentation challenges due to low visibility of fine structures.
- Existing methods for non-contrast CT often yield high recall but low precision, limiting their application beyond calcium scoring.
Purpose of the Study:
- To develop and evaluate a deep learning algorithm for segmenting coronary arteries in multi-vendor, ECG-gated, non-contrast cardiac CT images.
- To introduce a novel semi-automatic framework for generating Ground Truth (GT) data using image registration, aiming for increased efficiency and data diversity.
- To propose a new method for manual mesh-to-image registration for creating a robust test-GT dataset.
Main Methods:
- A deep learning algorithm was developed using an AutoML framework.
- A semi-automatic GT generation process leveraging image registration was implemented for efficient data creation.
- A novel manual mesh-to-image registration technique was employed to establish a high-quality test-GT dataset for evaluation.
Main Results:
- The proposed deep learning model demonstrated significantly more accurate delineation of coronary arteries compared to the training GT.
- The segmentation performance achieved Dice and clDice metrics comparable to interrater variability.
- The semi-automatic GT generation framework proved efficient in creating large, diverse datasets for model training.
Conclusions:
- The developed deep learning algorithm effectively segments coronary arteries in non-contrast cardiac CT, addressing a significant research gap.
- The novel semi-automatic GT generation method enhances efficiency and data diversity, leading to well-generalizing models.
- This approach holds promise for improving the medical assessment of heart pathologies using less invasive CT imaging.
Abstract:
Precise localization of coronary arteries in Computed Tomography (CT) scans is critical from the perspective of medical assessment of various heart pathologies. Although manifold methods exist that offer high-quality segmentation of coronary arteries in cardiac contrast-enhanced CT scans, the potential of less invasive, non-contrast CT is still not fully exploited. Since such fine anatomical structures are hardly visible in this type of medical image, the existing methods are characterized by high recall and low precision, and are used mainly for filtering of calcified atherosclerotic plaques in the context of calcium scoring. In this paper, we address this research gap and introduce a deep learning algorithm for segmenting coronary arteries in multi-vendor ECG-gated non-contrast cardiac CT images which benefits from a novel framework for semi-automatic generation of Ground Truth (GT) via image registration. We hypothesize that the proposed GT generation process is much more efficient in this case than manual segmentation, as it allows for a fast generation of large volumes of diverse data, which translates to well-generalizing models. To thoroughly evaluate the segmentation quality based on such an approach, we propose a novel method for manual mesh-to-image registration, which is used to create our test-GT. The experimental study shows that our AutoML-powered deep machine learning model delineates the coronary arteries significantly more accurately than the GT used for its training, and leads to the Dice and clDice metrics close to the interrater variability.
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