Department of Software and IT Engineering, École de technologie supérieure, Montreal, Canada.
Division of Cardiology, Hôpital Pierre Boucher, Longueuil, Canada.
Med Phys. 2021 Jul;48(7):3511-3524. doi: 10.1002/mp.14909. Epub 2021 May 24.
Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm. But interpretation of the acquired images is operator dependent, which is not only very time-consuming but also highly error prone from one observer to another. An automatic and accurate coronary plaque tagging using OCT image post-processing can contribute to wide adoption of the OCT system and reducing the diagnostic error rate.
In this study, we propose a combination of spatial pyramid pooling module with dilated convolutions for semantic segmentation to extract atherosclerotic tissues regardless of their types and training a sparse auto-encoder to reconstruct the input features and enlarge the training data as well as plaque type characterization in OCT images.
The results demonstrate high precision of the proposed model with reduced computational complexity, which can be appropriate for real-time analysis of OCT images. At each step of the work, measured accuracy, sensitivity, specificity of more than 93% demonstrate high performance of the model.
The main focus of this study is atherosclerotic tissue characterization using OCT imaging. This contributes to wide adoption of the OCT imaging system by providing clinicians with a fully automatic interpretation of various atherosclerotic tissues. Future studies will be focused on analyzing atherosclerotic vulnerable plaques, those coronary plaques which are prone to rupture.
冠状动脉事件主要与成年人群中的动脉粥样硬化有关，动脉粥样硬化被认为是动脉壁组织中斑块的积累。光学相干断层扫描（OCT）是一种基于光的成像系统，用于分析冠状动脉组织层和包括斑块积累在内的病理形成。这种最先进的基于导管的成像系统提供了具有 10-15 µm 高分辨率的冠状动脉横截面图像。但是，对获得的图像的解释依赖于操作者，不仅非常耗时，而且从一个观察者到另一个观察者非常容易出错。使用 OCT 图像后处理对冠状动脉斑块进行自动且准确的标记，可以促进 OCT 系统的广泛采用，并降低诊断错误率。
在这项研究中，我们提出了一种将空间金字塔池化模块与扩张卷积相结合的方法，用于语义分割，以提取动脉粥样硬化组织，而不管其类型如何，并训练稀疏自编码器来重建输入特征并扩大训练数据，以及 OCT 图像中的斑块类型特征化。
结果表明，该模型具有较高的精度和降低的计算复杂度，适用于 OCT 图像的实时分析。在工作的每个步骤中，测量精度、敏感性和特异性均超过 93%，证明了模型的高性能。
本研究的重点是使用 OCT 成像对动脉粥样硬化组织进行特征描述。这有助于通过为临床医生提供各种动脉粥样硬化组织的全自动解释来广泛采用 OCT 成像系统。未来的研究将集中在分析易破裂的动脉粥样硬化脆弱斑块上，即容易破裂的冠状动脉斑块。
