期刊
INTERNATIONAL JOURNAL OF CARDIOLOGY
卷 172, 期 3, 页码 568-580出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.ijcard.2014.01.071
关键词
Optical coherence tomography; Image processing; Image segmentation; Method comparison study
资金
- European Commission, Marie Curie International Reintegration Grant, Project: SMILE [249303]
- General Secretariat of Research and Technology, Program: Heracleitus II, Athens, Greece
- Behrakis Foundation, Boston, USA
Objectives: 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. Methods: 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. Results: 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. Conclusions: 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. (C) 2014 Elsevier Ireland Ltd. All rights reserved.
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