28 February 2013 Automatic stent strut detection in intravascular OCT images using image processing and classification technique
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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.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hong Lu, Hong Lu, Madhusudhana Gargesha, Madhusudhana Gargesha, Zhao Wang, Zhao Wang, Daniel Chamie, Daniel Chamie, Guilherme F. Attizani, Guilherme F. Attizani, Tomoaki Kanaya, Tomoaki Kanaya, Soumya Ray, Soumya Ray, Marco A. Costa, Marco A. Costa, Andrew M. Rollins, Andrew M. Rollins, Hiram G. Bezerra, Hiram G. Bezerra, David L. Wilson, 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); doi: 10.1117/12.2007183; https://doi.org/10.1117/12.2007183
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