Automated segmentation of normal and diseased coronary arteries - The ASOCA challenge
| Autor: | Ramtin Gharleghi, Dona Adikari, Katy Ellenberger, Sze-Yuan Ooi, Chris Ellis, Chung-Ming Chen, Ruochen Gao, Yuting He, Raabid Hussain, Chia-Yen Lee, Jun Li, Jun Ma, Ziwei Nie, Bruno Oliveira, Yaolei Qi, Youssef Skandarani, João L. Vilaça, Xiyue Wang, Sen Yang, Arcot Sowmya, Susann Beier |
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| Rok vydání: | 2021 |
| Předmět: |
Image segmentation
Radiological and Ultrasound Technology Computed Tomography Angiography Health Informatics Coronary Artery Disease Coronary Angiography Computer Graphics and Computer-Aided Design Coronary Vessels Coronary arteries Machine learning Humans Radiology Nuclear Medicine and imaging Computer Vision and Pattern Recognition Tomography X-Ray Computed Algorithms |
| Zdroj: | Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 97 |
| ISSN: | 1879-0771 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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