ARCOCT：血管内光学相干断层扫描（OCT）图像中管腔边界的自动检测
ARCOCT: Automatic detection of lumen border in intravascular OCT images.
作者信息
Cheimariotis Grigorios-Aris, Chatzizisis Yiannis S, Koutkias Vassilis G, Toutouzas Konstantinos, Giannopoulos Andreas, Riga Maria, Chouvarda Ioanna, Antoniadis Antonios P, Doulaverakis Charalambos, Tsamboulatidis Ioannis, Kompatsiaris Ioannis, Giannoglou George D, Maglaveras Nicos
机构信息
Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Institute of Applied Biosciences, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece.
Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA.
出版信息
Comput Methods Programs Biomed. 2017 Nov;151:21-32. doi: 10.1016/j.cmpb.2017.08.007. Epub 2017 Aug 16.
BACKGROUND AND OBJECTIVE
Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images.
METHODS
ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts.
RESULTS
ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics.
CONCLUSIONS
ARCOCT allows accurate and fully-automated lumen border detection in OCT images.
背景与目的
血管内光学相干断层扫描（OCT）是检测动脉壁病理特征及研究支架置入后并发症的重要工具。OCT图像中的计算管腔边界检测具有很大优势，因为它有助于快速进行形态计量分析。然而，自动检测极具挑战性，因为OCT图像通常包含各种影响图像清晰度的伪影，包括侧支和管腔内血液等特征。本文介绍了ARCOCT，一种用于全自动检测OCT图像中管腔边界的分割方法。
方法
ARCOCT依赖多个连续的处理步骤，包括图像预处理、轮廓提取和细化。具体而言，对于轮廓提取，ARCOCT基于组织的反射率和吸收率等物理特性对OCT图像进行变换；对于轮廓细化，采用加权线性最小二乘法和二次多项式模型进行局部回归，以实现伪影和小分支校正以及动脉网格的平滑处理。我们的主要重点是在各种类型的OCT图像中实现准确的轮廓描绘，即在存在分支和伪影的具有挑战性的情况下也能做到。
结果
在从20名患者获取的1812幅图像（308幅来自支架置入段，1504幅来自天然段）的数据集中对ARCOCT进行了评估。使用标准统计分析方法，将ARCOCT与专家进行的基于各种几何特征（如面积、周长、半径、直径、质心等）和闭合轮廓匹配指标（Dice指数、豪斯多夫距离和无向平均距离）的地面真值手动分割进行比较。结果表明，所提出的方法非常有效且接近地面真值，在大多数检查指标上显示出无统计学显著差异。
结论
ARCOCT能够在OCT图像中实现准确且全自动的管腔边界检测。
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