Chatzizisis Yiannis S, Koutkias Vassilis G, Toutouzas Konstantinos, Giannopoulos Andreas, Chouvarda Ioanna, Riga Maria, Antoniadis Antonios P, Cheimariotis Grigorios, Doulaverakis Charalampos, Tsampoulatidis Ioannis, Bouki Konstantina, Kompatsiaris Ioannis, Stefanadis Christodoulos, Maglaveras Nicos, Giannoglou George D
Cardiovascular Engineering and Atherosclerosis Laboratory, First Department of Cardiology, AHEPA University Hospital, Aristotle University Medical School, Thessaloniki, Greece.
Laboratory of Medical Informatics, Aristotle University Medical School, Thessaloniki, Greece.
Int J Cardiol. 2014 Apr 1;172(3):568-80. doi: 10.1016/j.ijcard.2014.01.071. Epub 2014 Jan 24.
The analysis of intracoronary optical coherence tomography (OCT) images is based on manual identification of the lumen contours and relevant structures. However, manual image segmentation is a cumbersome and time-consuming process, subject to significant intra- and inter-observer variability. This study aims to present and validate a fully-automated method for segmentation of intracoronary OCT images.
We studied 20 coronary arteries (mean length=39.7±10.0 mm) from 20 patients who underwent a clinically-indicated cardiac catheterization. The OCT images (n=1812) were segmented manually, as well as with a fully-automated approach. A semi-automated variation of the fully-automated algorithm was also applied. Using certain lumen size and lumen shape characteristics, the fully- and semi-automated segmentation algorithms were validated over manual segmentation, which was considered as the gold standard.
Linear regression and Bland-Altman analysis demonstrated that both the fully-automated and semi-automated segmentation had a very high agreement with the manual segmentation, with the semi-automated approach being slightly more accurate than the fully-automated method. The fully-automated and semi-automated OCT segmentation reduced the analysis time by more than 97% and 86%, respectively, compared to manual segmentation.
In the current work we validated a fully-automated OCT segmentation algorithm, as well as a semi-automated variation of it in an extensive "real-life" dataset of OCT images. The study showed that our algorithm can perform rapid and reliable segmentation of OCT images.
冠状动脉光学相干断层扫描（OCT）图像分析基于对管腔轮廓和相关结构的手动识别。然而，手动图像分割是一个繁琐且耗时的过程，存在显著的观察者内和观察者间差异。本研究旨在提出并验证一种用于冠状动脉OCT图像分割的全自动方法。
我们研究了20例接受临床指征心脏导管插入术患者的20条冠状动脉（平均长度 = 39.7±10.0 mm）。对OCT图像（n = 1812）进行了手动分割以及全自动分割。还应用了全自动算法的半自动变体。利用特定的管腔大小和管腔形状特征，将全自动和半自动分割算法与被视为金标准的手动分割进行了验证。
线性回归和Bland-Altman分析表明，全自动和半自动分割与手动分割均具有高度一致性，半自动方法比全自动方法略更准确。与手动分割相比，全自动和半自动OCT分割分别将分析时间减少了97%以上和86%。
在当前工作中，我们在一个广泛的OCT图像“真实生活”数据集中验证了一种全自动OCT分割算法及其半自动变体。研究表明，我们的算法能够对OCT图像进行快速且可靠的分割。
