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Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge
Ramtin Gharleghi1, Dona Adikari2, Katy Ellenberger2
1School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, Australia.
Insights
Fully automatic segmentation of coronary arteries from Computed Tomography Coronary Angiography (CTCA) is now possible. This breakthrough enables large-scale, personalized clinical applications for cardiovascular disease research and patient care.
Area of Science:
- Medical Imaging
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
Background:
- Cardiovascular disease remains a leading global cause of mortality.
- Computed Tomography Coronary Angiography (CTCA) is crucial for evaluating coronary artery disease and cardiac structures.
- Current segmentation methods for coronary arteries are often manual or semi-automatic, limiting scalability and clinical integration.
Purpose of the Study:
- To present the first challenge focused on developing fully automatic segmentation methods for entire coronary artery trees.
- To establish a large, standardized dataset of normal and diseased coronary arteries.
- To create a benchmark for automated CTCA processing.
Main Methods:
- Development of fully automatic algorithms for coronary artery segmentation.
- Creation of a comprehensive dataset of annotated coronary artery trees from CTCA.
- Evaluation of segmentation performance on a standardized benchmark.
Main Results:
- Successful development of fully automatic segmentation methods for complete coronary artery trees.
- Establishment of the first large, standardized dataset for coronary artery segmentation.
- Creation of a new benchmark for automated CTCA processing.
Conclusions:
- Fully automatic coronary artery segmentation from CTCA is achievable.
- The developed dataset and benchmark facilitate large-scale and personalized clinical applications.
- This advancement has significant implications for cardiovascular disease research and patient management.
Abstract:
Cardiovascular disease is a major cause of death worldwide. Computed Tomography Coronary Angiography (CTCA) is a non-invasive method used to evaluate coronary artery disease, as well as evaluating and reconstructing heart and coronary vessel structures. Reconstructed models have a wide array of for educational, training and research applications such as the study of diseased and non-diseased coronary anatomy, machine learning based disease risk prediction and in-silico and in-vitro testing of medical devices. However, coronary arteries are difficult to image due to their small size, location, and movement, causing poor resolution and artefacts. Segmentation of coronary arteries has traditionally focused on semi-automatic methods where a human expert guides the algorithm and corrects errors, which severely limits large-scale applications and integration within clinical systems. International challenges aiming to overcome this barrier have focussed on specific tasks such as centreline extraction, stenosis quantification, and segmentation of specific artery segments only. Here we present the results of the first challenge to develop fully automatic segmentation methods of full coronary artery trees and establish the first large standardized dataset of normal and diseased arteries. This forms a new automated segmentation benchmark allowing the automated processing of CTCAs directly relevant for large-scale and personalized clinical applications.
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