Automatic stent detection in intravascular OCT images using bagged decision trees

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.