Detection, Grading and Classification of Coronary Stenoses in Computed Tomography Angiography

Recently conducted clinical studies prove the utility of Coronary Computed Tomography Angiography (CCTA) as a viable alternative to invasive angiography for the detection of Coronary Artery Disease (CAD). This has lead to the development of several algorithms for automatic detection and grading of coronary stenoses. However, most of these methods focus on detecting calcified plaques only. A few methods that can also detect and grade non-calcified plaques require substantial user involvement. In this paper, we propose a fast and fully automatic system that is capable of detecting, grading and classifying coronary stenoses in CCTA caused by all types of plaques. We propose a four-step approach including a learning-based centerline verification step and a lumen cross-section estimation step using random regression forests. We show state-of-the-art performance of our method in experiments conducted on a set of 229 CCTA volumes. With an average processing time of 1.8 seconds per case after centerline extraction, our method is significantly faster than competing approaches.

[1]  S. Abbara,et al.  Cardiac CT: State of the art for the detection of coronary arterial stenosis , 2008 .

[2]  Michael Scheuering,et al.  Shape-based segmentation and visualization techniques for evaluation of atherosclerotic plaques in coronary artery disease , 2006, SPIE Medical Imaging.

[3]  Xiaodong Wu,et al.  Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic Approach , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  M. Schaap,et al.  3D Segmentation in the Clinic: A Grand Challenge II - Coronary Artery Tracking , 2008, The MIDAS Journal.

[5]  Hüseyin Tek,et al.  Robust Vessel Tree Modeling , 2008, MICCAI.

[6]  B. Ginneken,et al.  3D Segmentation in the Clinic: A Grand Challenge , 2007 .

[7]  Stefan Wesarg,et al.  Localizing Calcifications in Cardiac CT Data Sets Using a New Vessel Segmentation Approach , 2006, Journal of Digital Imaging.

[8]  D. Mozaffarian,et al.  Heart disease and stroke statistics--2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. , 2009, Circulation.

[9]  E. Halpern,et al.  Diagnosis of coronary stenosis with CT angiography comparison of automated computer diagnosis with expert readings. , 2011, Academic radiology.

[10]  Filippo Alberghina,et al.  Learning curve for coronary CT angiography: what constitutes sufficient training? , 2009, Radiology.

[11]  G. Funka-Lea,et al.  Author ' s personal copy A review of 3 D vessel lumen segmentation techniques : Models , features and extraction schemes , 2009 .

[12]  Gabor Fichtinger,et al.  Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 11th International Conference, New York, NY, USA, September 6-10, 2008, Proceedings, Part I , 2008, International Conference on Medical Image Computing and Computer-Assisted Intervention.

[13]  Martin Styner,et al.  Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms , 2009, Medical Image Anal..

[14]  Dorin Comaniciu,et al.  Fast Automatic Detection of Calcified Coronary Lesions in 3D Cardiac CT Images , 2010, MLMI.

[15]  Willi A. Kalender,et al.  Dual-source cardiac computed tomography: image quality and dose considerations , 2008, European Radiology.

[16]  Günther Greiner,et al.  Learning-Based Detection of Stenotic Lesions in Coronary CT Data , 2008, VMV.

[17]  G. Raff,et al.  Use of multislice CT for the evaluation of emergency room patients with chest pain: The so‐called “Triple rule‐out” , 2008, Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions.

[18]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.