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Automatic stent detection in intravascular OCT images using bagged decision trees

2012; Optica Publishing Group; Volume: 3; Issue: 11 Linguagem: Inglês

10.1364/boe.3.002809

2156-7085

Hong Lu, Madhusudhana Gargesha, Zhao Wang, Daniel Chamié, Guilherme F. Attizani, Tomoaki Kanaya, Soumya Ray, Marco A. Costa, Andrew M. Rollins, Hiram G. Bezerra, David L. Wilson,

Optical Coherence Tomography Applications

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.