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Published on: November 30, 2022
A Computationally Efficient Approach to Segmentation of the Aorta and Coronary Arteries Using Deep Learning
Wing Keung Cheung1,2, Robert Bell3, Arjun Nair4
1Centre for Medical Image ComputingUniversity College London London WC1V 6LJ U.K.
Insights
Automated deep learning accurately identifies coronary artery narrowing on CTCA scans. This efficient model aids early diagnosis of coronary artery disease without needing specialized hardware, improving patient outcomes.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Cardiology
Background:
- Early detection of coronary artery disease (CAD) is crucial for preventing heart attacks.
- Computed tomography coronary angiography (CTCA) is optimal for visualizing coronary arteries.
- A shortage of radiologists hinders timely CAD diagnosis, especially in emergency settings.
Purpose of the Study:
- To develop a computationally efficient, automated deep learning model for rapid and accurate identification of coronary artery narrowing on CTCA images.
- To address the need for diagnostic tools deployable in standard hospital settings without graphical processing units.
Main Methods:
- A fully automatic two-dimensional Unet model was developed to segment the aorta and coronary arteries in CTCA images.
- Two segmentation models were trained: one for the aorta and coronary arteries, and another for coronary arteries alone.
- Model efficiency was optimized for deployment on typical hospital servers lacking graphical processing units.
Main Results:
- The proposed model achieved 91.20% and 88.80% dice similarity coefficient accuracy for the two segmentation tasks, respectively.
- The automated method outperformed a semi-automatic approach when segmenting coronary arteries exclusively.
- Performance was comparable to existing 2D and 3D deep learning models, with significant computational and memory efficiency.
Conclusions:
- The developed automated segmentation model offers a viable, efficient solution for detecting coronary artery narrowing on CTCA.
- Its ability to run without graphical processing units facilitates deployment in resource-constrained hospital environments.
- This technology supports timely CAD diagnosis, potentially reducing heart attack risks.
Abstract:
Early detection and diagnosis of coronary artery disease could reduce the risk of developing a heart attack. The coronary arteries are optimally visualised using computed tomography coronary angiography (CTCA) imaging. These images are reviewed by specialist radiologists who evaluate the coronary arteries for potential narrowing. A lack of radiologists in the UK is a constraint to timely diagnosis of coronary artery disease, particularly in the acute accident and emergency department setting. The development of automated methods by which coronary artery narrowing can be identified rapidly and accurately are therefore timely. Such complex computer based tools also need to be sufficiently computationally efficient that they can run on servers typically found in hospital settings, where graphical processing units for example are unavailable. We propose a fully automatic two-dimensional Unet model to segment the aorta and coronary arteries on CTCA images. Two models are trained to segment two regions of interest, (1) the aorta and the coronary arteries or (2) the coronary arteries alone. Our method achieves 91.20% and 88.80% dice similarity coefficient accuracy on regions of interest 1 and 2 respectively. Compared with a semi-automatic segmentation method, our model performs better when segmenting the coronary arteries alone. The performance of the proposed method is comparable to existing published two-dimensional or three-dimensional deep learning models. Importantly, the algorithmic and graphical processing unit memory efficiencies are maintained such that the model can be deployed without requiring graphical processing units, and therefore can be used in a hospital setting.
