18 March 2014 Learning-based automatic detection of severe coronary stenoses in CT angiographies
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Abstract
3D cardiac computed tomography angiography (CCTA) is becoming a standard routine for non-invasive heart diseases diagnosis. Thanks to its high negative predictive value, CCTA is increasingly used to decide whether or not the patient should be considered for invasive angiography. However, an accurate assessment of cardiac lesions using this modality is still a time consuming task and needs a high degree of clinical expertise. Thus, providing automatic tool to assist clinicians during the diagnosis task is highly desirable. In this work, we propose a fully automatic approach for accurate severe cardiac stenoses detection. Our algorithm uses the Random Forest classi cation to detect stenotic areas. First, the classi er is trained on 18 CT cardiac exams with CTA reference standard. Then, then classi cation result is used to detect severe stenoses (with a narrowing degree higher than 50%) in a 30 cardiac CT exam database. Features that best captures the di erent stenoses con guration are extracted along the vessel centerlines at di erent scales. To ensure the accuracy against the vessel direction and scale changes, we extract features inside cylindrical patterns with variable directions and radii. Thus, we make sure that the ROIs contains only the vessel walls. The algorithm is evaluated using the Rotterdam Coronary Artery Stenoses Detection and Quantication Evaluation Framework. The evaluation is performed using reference standard quanti cations obtained from quantitative coronary angiography (QCA) and consensus reading of CTA. The obtained results show that we can reliably detect severe stenosis with a sensitivity of 64%.
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Imen Melki, Cyril Cardon, Nicolas Gogin, Hugues Talbot, Laurent Najman, "Learning-based automatic detection of severe coronary stenoses in CT angiographies", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903536 (18 March 2014); doi: 10.1117/12.2043274; https://doi.org/10.1117/12.2043274
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