Cao Yihui, Jin Qinhua, Lu Yifeng, Jing Jing, Chen Yundai, Yin Qinye, Qin Xianjing, Li Jianan, Zhu Rui, Zhao Wei
State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, Shaanxi, China.
School of the Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Biomed Opt Express. 2018 May 1;9(6):2495-2510. doi: 10.1364/BOE.9.002495. eCollection 2018 Jun 1.
The bioresorbable vascular scaffold (BVS) is a new generation of bioresorbable scaffold (BRS) for the treatment of coronary artery disease. A potential challenge of BVS is malapposition, which may possibly lead to late stent thrombosis. It is therefore important to conduct malapposition analysis right after stenting. Since an intravascular optical coherence tomography (IVOCT) image sequence contains thousands of BVS struts, manual analysis is labor intensive and time consuming. Computer-based automatic analysis is an alternative, but faces some difficulties due to the interference of blood artifacts and the uncertainty of the struts number, position and size. In this paper, we propose a novel framework for a struts malapposition analysis that breaks down the problem into two steps. Firstly, struts are detected by a cascade classifier trained by AdaBoost and a region of interest (ROI) is determined for each strut to completely contain it. Then, strut boundaries are segmented within ROIs through dynamic programming. Based on the segmentation result, malapposition analysis is conducted automatically. Tested on 7 pullbacks labeled by an expert, our method correctly detected 91.5% of 5821 BVS struts with 12.1% false positives. The average segmentation Dice coefficient for correctly detected struts was 0.81. The time consumption for a pullback is 15 on average. We conclude that our method is accurate and efficient for BVS strut detection and segmentation, and enables automatic BVS malapposition analysis in IVOCT images.
生物可吸收血管支架（BVS）是用于治疗冠状动脉疾病的新一代生物可吸收支架（BRS）。BVS的一个潜在挑战是贴壁不良，这可能会导致晚期支架内血栓形成。因此，在支架置入后立即进行贴壁不良分析很重要。由于血管内光学相干断层扫描（IVOCT）图像序列包含数千个BVS支架，手动分析工作量大且耗时。基于计算机的自动分析是一种替代方法，但由于血液伪影的干扰以及支架数量、位置和尺寸的不确定性而面临一些困难。在本文中，我们提出了一种用于支架贴壁不良分析的新颖框架，该框架将问题分解为两个步骤。首先，通过由AdaBoost训练的级联分类器检测支架，并为每个支架确定一个感兴趣区域（ROI）以完全包含它。然后，通过动态规划在ROI内分割支架边界。基于分割结果，自动进行贴壁不良分析。在由专家标记的7个回撤数据上进行测试，我们的方法正确检测出5821个BVS支架中的91.5%，误报率为12.1%。正确检测出的支架的平均分割Dice系数为0.81。一次回撤的平均耗时为15秒。我们得出结论，我们的方法对于BVS支架检测和分割是准确且高效的，并且能够在IVOCT图像中实现自动BVS贴壁不良分析。
