University of Ioannina, Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, GR 45110 Ioannina, Greece.
ThoraxCenter, Erasmus Medical Center, 's-Gravendijkwal 230, 3015 CE Rotterdam, The Netherlands.
J Biomed Opt. 2014 Feb;19(2):026009. doi: 10.1117/1.JBO.19.2.026009.
Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson's correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts' annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.
光学相干断层扫描（OCT）是一种基于光的冠状动脉内成像方式，可提供管腔和斑块形态的高分辨率横截面图像。目前，OCT 图像的分割和斑块成分的识别主要由专家观察者手动完成。然而，这个过程既繁琐又耗时，其准确性依赖于观察者的专业知识。为了解决这些限制，我们提出了一种能够全自动处理 OCT 数据的方法。该方法能够在 OCT 帧中检测到管腔边界，识别斑块区域，并检测四种组织类型：钙（CA）、脂质组织（LT）、纤维组织（FT）和混合组织（MT）。使用 22 名患者的 27 次 OCT 拉回的注释评估了所开发方法的效率。所开发方法的输出与手动注释之间的 Pearson 相关系数非常高（从 0.96 到 0.99），而 Bland-Altman 分析显示没有显著的偏差和良好的一致性区间。专家注释和方法在检测 CA、LT、FT 和 MT 方面的重叠区域比例分别为 0.81、0.71、0.87 和 0.81。
