Shalev Ronny, Bezerra Hiram G, Ray Soumya, Prabhu David, Wilson David L
Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH, 44106, USA.
Cardiovascular Imaging Core Laboratory, Harrington Heart & Vascular Institute, University Hospitals Case Medical Center, Cleveland, OH, 44106, USA.
Proc SPIE Int Soc Opt Eng. 2016 Mar;9786. doi: 10.1117/12.2216315.
The presence of extensive calcification is a primary concern when planning and implementing a vascular percutaneous intervention such as stenting. If the balloon does not expand, the interventionalist must blindly apply high balloon pressure, use an atherectomy device, or abort the procedure. As part of a project to determine the ability of Intravascular Optical Coherence Tomography (IVOCT) to aid intervention planning, we developed a method for automatic classification of calcium in coronary IVOCT images. We developed an approach where plaque texture is modeled by the joint probability distribution of a bank of filter responses where the filter bank was chosen to reflect the qualitative characteristics of the calcium. This distribution is represented by the frequency histogram of filter response cluster centers. The trained algorithm was evaluated on independent ex-vivo image data accurately labeled using registered 3D microscopic cryo-image data which was used as ground truth. In this study, regions for extraction of sub-images (SI's) were selected by experts to include calcium, fibrous, or lipid tissues. We manually optimized algorithm parameters such as choice of filter bank, size of the dictionary, etc. Splitting samples into training and testing data, we achieved 5-fold cross validation calcium classification with F score of 93.7±2.7% with recall of ≥89% and a precision of ≥97% in this scenario with admittedly selective data. The automated algorithm performed in close-to-real-time (2.6 seconds per frame) suggesting possible on-line use. This promising preliminary study indicates that computational IVOCT might automatically identify calcium in IVOCT coronary artery images.
在规划和实施诸如支架置入等血管介入手术时，广泛钙化的存在是一个主要问题。如果球囊无法扩张，介入医生必须盲目施加高球囊压力、使用旋切装置或中止手术。作为确定血管内光学相干断层扫描（IVOCT）辅助介入规划能力项目的一部分，我们开发了一种对冠状动脉IVOCT图像中的钙化进行自动分类的方法。我们开发了一种方法，通过一组滤波器响应的联合概率分布对斑块纹理进行建模，其中滤波器组的选择是为了反映钙化的定性特征。这种分布由滤波器响应聚类中心的频率直方图表示。使用注册的3D微观冷冻图像数据作为地面真值，对独立的离体图像数据进行准确标记，对训练好的算法进行评估。在本研究中，由专家选择用于提取子图像（SI）的区域，以包括钙化、纤维或脂质组织。我们手动优化算法参数，如滤波器组的选择、字典大小等。将样本分为训练数据和测试数据，在这种有选择性数据的情况下，我们实现了5折交叉验证钙分类，F分数为93.7±2.7%，召回率≥89%，精度≥97%。自动算法以接近实时的速度运行（每帧2.6秒），表明可能可以在线使用。这项有前景的初步研究表明，计算IVOCT可能会自动识别IVOCT冠状动脉图像中的钙化。
