Automatic stent strut detection in intravascular OCT images using image processing and classification technique

Hong Lu; Madhusudhana Gargesha; Zhao Wang; Daniel Chamie; Guilherme F. Attizani; Tomoaki Kanaya; Soumya Ray; Marco A. Costa; Andrew M. Rollins; Hiram G. Bezerra; David L. Wilson

doi:10.1117/12.2007183

28 February 2013 Automatic stent strut detection in intravascular OCT images using image processing and classification technique

Hong Lu, Madhusudhana Gargesha, Zhao Wang, Daniel Chamie, Guilherme F. Attizani, Tomoaki Kanaya, Soumya Ray, Marco A. Costa, Andrew M. Rollins, Hiram G. Bezerra, David L. Wilson

Author Affiliations +

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 867015 (2013) https://doi.org/10.1117/12.2007183
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Intravascular OCT (iOCT) is an imaging modality with ideal resolution and contrast to provide accurate in vivo assessments of tissue healing following stent implantation. Our Cardiovascular Imaging Core Laboratory has served >20 international stent clinical trials with >2000 stents analyzed. Each stent requires 6-16hrs of manual analysis time and we are developing highly automated software to reduce this extreme effort. Using classification technique, physically meaningful image features, forward feature selection to limit overtraining, and leave-one-stent-out cross validation, we detected stent struts. To determine tissue coverage areas, we estimated stent “contours” by fitting detected struts and interpolation points from linearly interpolated tissue depths to a periodic cubic spline. Tissue coverage area was obtained by subtracting lumen area from the stent area. Detection was compared against manual analysis of 40 pullbacks. We obtained recall = 90±3% and precision = 89±6%. When taking struts deemed not bright enough for manual analysis into consideration, precision improved to 94±6%. This approached inter-observer variability (recall = 93%, precision = 96%). Differences in stent and tissue coverage areas are 0.12 ± 0.41 mm² and 0.09 ± 0.42 mm², respectively. We are developing software which will enable visualization, review, and editing of automated results, so as to provide a comprehensive stent analysis package. This should enable better and cheaper stent clinical trials, so that manufacturers can optimize the myriad of parameters (drug, coverage, bioresorbable versus metal, etc.) for stent design.

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Hong Lu, Madhusudhana Gargesha, Zhao Wang, Daniel Chamie, Guilherme F. Attizani, Tomoaki Kanaya, Soumya Ray, Marco A. Costa, Andrew M. Rollins, Hiram G. Bezerra, and David L. Wilson "Automatic stent strut detection in intravascular OCT images using image processing and classification technique", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867015 (28 February 2013); https://doi.org/10.1117/12.2007183

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