Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images.
| Title | Clinical validation of an algorithm for rapid and accurate automated segmentation of intracoronary optical coherence tomography images. |
| Publication Type | Journal Article |
| Year of Publication | 2014 |
| Authors | Chatzizisis, Y. S., Koutkias V. G., Toutouzas K., Giannopoulos A., Chouvarda I., Riga M., Antoniadis A. P., Cheimariotis G., Doulaverakis C., Tsampoulatidis I., Bouki K., Kompatsiaris I., Stefanadis C., Maglaveras N., & Giannoglou G. D. |
| Journal | Int J Cardiol |
| Volume | 172 |
| Issue | 3 |
| Pagination | 568-80 |
| Date Published | 2014 Apr 1 |
| ISSN | 1874-1754 |
| Keywords | Algorithms, Cardiac Catheterization, Coronary Artery Disease, Coronary Vessels, Female, Humans, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Middle Aged, Reproducibility of Results, ROC Curve, Time Factors, Tomography, Optical Coherence |
| Abstract | 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. |
| DOI | 10.1016/j.ijcard.2014.01.071 |
| Alternate Journal | Int. J. Cardiol. |
| PubMed ID | 24529948 |
