D Analysis of Thin-Cap Fibroatheromas by an Automatic Graph-Based Approach in Intravascular Optical Coherence Tomography

Background: Thin-cap fibroatheromas (TCFAs) are the most well-known reason for plaque rupture leading to the acute coronary syndrome. Although optical coherence tomography (OCT) has the potential for use in the identification of TCFAs, the conventional analysis of this modality alone in the 2D domain is not sufficient for detecting TCFAs. Objectives: The present study proposes a fully-automated method for the 3D analysis of vulnerable plaques, especially TCFAs, in OCT sequence frames. Methods: A new 2-step graph-based method was used to extract the 3D morphology of the fibrous cap in an intravascular OCT image sequence. A linear cost function was applied by adding novel hard constraints. Then, an undirected graph was performed with specified edge weighting. The min-cut problem was solved for segmentation. It was divided into 2 phases: The former extracted a media region from the lumen region, and the latter extracted the fibrous cap from the media. Finally, the TCFA was extracted by the quantification of the fibrous cap thickness. Results: The method was validated using 3 sets of OCT raw image sequences. The proposed method was evaluated on an OCT dataset. It was composed of 3 groups of 264 consecutive intravascular OCT frames acquired from the left coronary artery. On the real data, the lumen diameter and 3D TCFA thickness achieved 88.28% and 85.5% accuracy, respectively, in comparison with manual segmentation. Conclusions: An appropriate correlation was obtained between the TCFA detected by the proposed method and the one selected manually. The proposed method was able to speed up atherosclerosis assessment; therefore, it can be used to improve the management of the acute coronary syndrome.