Objectives: Fibrous cap thickness is the most critical component of plaque stability. Therefore, in vivo quantification of cap thickness could yield valuable information for estimating the risk of plaque rupture. In the context of preoperative planning and perioperative decision making, intracoronary optical coherence tomography imaging can provide a very detailed characterization of the arterial wall structure. However, visual interpretation of the images is laborious, subject to variability, and therefore not always sufficiently reliable for immediate decision of treatment. Methods: A novel semiautomatic segmentation method to quantify coronary fibrous cap thickness in optical coherence tomography is introduced. To cope with the most challenging issue when estimating cap thickness (namely the diffuse appearance of the anatomical abluminal interface to be detected), the proposed method is based on a robust dynamic programming framework using a geometrical a priori. To determine the optimal parameter settings, a training phase was conducted on 10 patients. Results: Validated on a dataset of 179 images from 21 patients, the present framework could successfully extract the fibrous cap contours. When assessing minimal cap thickness, segmentation results from the proposed method were in good agreement with the reference tracings performed by a medical expert (mean absolute error and standard deviation of 22±18μm,R=.73) and were similar to inter-observer reproducibility (21±19μm, R = .74), while being significantly faster and fully reproducible. Conclusion: The proposed framework demonstrated promising performances and could potentially be used for online identification of high-risk plaques.

Original languageEnglish
Pages (from-to)1383-1394
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume10
Issue number9
DOIs
Publication statusPublished - 2015
Externally publishedYes

    Research areas

  • Contour segmentation, Coronary artery, Dynamic programming, Interventional imaging, Optical coherence tomography, Preoperative planning, Thin-cap fibroatheroma

ID: 47147605