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...

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Bibliographic Details
Published in:Biomedical optics express 2012-11, Vol.3 (11), p.2809-2824
Main Authors: Lu, Hong, Gargesha, Madhusudhana, Wang, Zhao, Chamie, Daniel, Attizzani, Guilherme F, Kanaya, Tomoaki, Ray, Soumya, Costa, Marco A, Rollins, Andrew M, Bezerra, Hiram G, Wilson, David L
Format: Article
Language:English
Subjects:
Image Processing
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Summary: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.
ISSN:2156-7085
2156-7085
DOI:10.1364/BOE.3.002809