Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging—they are relatively invisible via angiography—and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images.
Optical coherence tomography (OCT) provides high-resolution cross-sectional images of arterial luminal morphology.
Traditionally lumen segmentation of OCT images is performed manually by expert observers; a laborious, time
consuming effort, sensitive to inter-observer variability process. Although several automated methods have been
developed, the majority cannot be applied in real time because of processing demands.
To address these limitations we propose a new method for rapid image segmentation of arterial lumen borders using
OCT images that involves the following steps: 1) OCT image acquisition using the raw OCT data, 2) reconstruction of
longitudinal cross-section (LOCS) images from four different acquisition angles, 3) segmentation of the LOCS images
and 4) lumen contour construction in each 2D cross-sectional image.
The efficiency of the developed method was evaluated using 613 annotated images from 10 OCT pullbacks acquired
from 10 patients at the time of coronary arterial interventions. High Pearson’s correlation coefficient was obtained when
lumen areas detected by the method were compared to areas annotated by experts (r=0.98, R2=0.96); Bland-Altman
analysis showed no significant bias with good limits of agreement.
The proposed methodology permits reliable border detection especially in lumen areas having artifacts and is faster than
traditional techniques making it capable of being used in real time applications. The method is likely to assist in a
number of research and clinical applications - further testing in an expanded clinical arena will more fully define the
limits and potential of this approach.
Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson’s correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts’ annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.