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Proceedings Paper

Automatic stent strut detection in intravascular OCT images using image processing and classification technique
Author(s): 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
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Paper 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.

Paper Details

Date Published: 28 February 2013
PDF: 8 pages
Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 867015 (28 February 2013); doi: 10.1117/12.2007183
Show Author Affiliations
Hong Lu, Case Western Reserve Univ. (United States)
Madhusudhana Gargesha, Case Western Reserve Univ. (United States)
Zhao Wang, Case Western Reserve Univ. (United States)
Daniel Chamie, Harrington Heart & Vascular Institute, Univ. Hospitals Case Medical Ctr. (United States)
Guilherme F. Attizani, Harrington Heart & Vascular Institute, Univ. Hospitals Case Medical Ctr. (United States)
Tomoaki Kanaya, Harrington Heart & Vascular Institute, Univ. Hospitals Case Medical Ctr. (United States)
Soumya Ray, Case Western Reserve Univ. (United States)
Marco A. Costa, Harrington Heart & Vascular Institute, Univ. Hospitals Case Medical Ctr. (United States)
Andrew M. Rollins, Case Western Reserve Univ. (United States)
Hiram G. Bezerra, Harrington Heart & Vascular Institute, Univ. Hospitals Case Medical Ctr. (United States)
David L. Wilson, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 8670:
Medical Imaging 2013: Computer-Aided Diagnosis
Carol L. Novak; Stephen Aylward, Editor(s)

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