Open AccessAutomatic stent detection in intravascular OCT images using bagged decision trees
Author(s) -
Hong Lu,
Madhusudhana Gargesha,
Zhao Wang,
Daniel Chamié,
Guilherme F. Attizzani,
Tomoaki Kanaya,
Soumya Ray,
Marco A. Costa,
Andrew M. Rollins,
Hiram G. Bezerra,
David L. Wilson
Publication year - 2012
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.3.002809
Subject(s) - optical coherence tomography , stent , computer science , artificial intelligence , decision tree , precision and recall , radiology , pattern recognition (psychology) , biomedical engineering , computer vision , medicine
Intravascular optical coherence tomography (iOCT) is being used to assess viability of new coronary artery stent designs. We developed a highly automated method for detecting stent struts and measuring tissue coverage. We trained a bagged decision trees classifier to classify candidate struts using features extracted from the images. With 12 best features identified by forward selection, recall (precision) were 90%-94% (85%-90%). Including struts deemed insufficiently bright for manual analysis, precision improved to 94%. Strut detection statistics approached variability of manual analysis. Differences between manual and automatic area measurements were 0.12 ± 0.20 mm(2) and 0.11 ± 0.20 mm(2) for stent and tissue areas, respectively. With proposed algorithms, analyst time per stent should significantly reduce from the 6-16 hours now required.