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 mm2 and 0.11 ± 0.20 mm2 for stent and tissue areas, respectively. With proposed algorithms, analyst time per stent should significantly reduce from the 6–16 hours now required.
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机译：血管内光学相干断层扫描（iOCT）被用于评估新冠状动脉支架设计的可行性。我们开发了一种高度自动化的方法来检测支架撑杆和测量组织覆盖率。我们训练了一个装袋的决策树分类器，使用从图像中提取的特征对候选支撑进行分类。通过正向选择确定了12个最佳功能，召回率（准确率）为90％–94％（85％–90％）。包括被认为不足以进行手动分析的支撑杆，精度提高到94％。支柱检测统计数据接近于人工分析的可变性。支架和组织区域的手动和自动区域测量之间的差异分别为0.12±0.20 mm 2 和0.11±0.20 mm 2 。使用建议的算法，每个支架的分析师时间应从现在所需的6-16小时大大减少。
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