Automatic detection of bioabsorbable coronary stents in IVUS images using a cascade of classifiers.
Bioabsorbable drug-eluting coronary stents present a very promising improvement to the common metallic ones solving some of the most important problems of stent implantation: the late restenosis. These stents made of poly-L-lactic acid cause a very subtle acoustic shadow (compared to the metallic ones) making difficult the automatic detection and measurements in images. In this paper, we propose a novel approach based on a cascade of GentleBoost classifiers to detect the stent struts using structural features to code the information of the different subregions of the struts. A stochastic gradient descent method is applied to optimize the overall performance of the detector. Validation results of struts detection are very encouraging with an average F-measure of 81%.
Rotger
David
D
Medical Imaging Laboratory, ComputerVision Center and the Computer Science Department, Autonomous University of Barcelona, 08193 Barcelona, Spain. rotger@cvc.uab.cat
Radeva
Petia
P
Bruining
Nico
N
D020341:Absorbable Implants; D000465:Algorithms; D001185:Artificial Intelligence; D003331:Coronary Vessels / Q000033:anatomy & histology*; D054855:Drug-Eluting Stents; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D015203:Reproducibility of Results; D013269:Stochastic Processes; D018084:Ultrasonography, Interventional / Q000379:methods*