Fully automatic three-dimensional visualization of intravascular optical coherence tomography images: methods and feasibility in vivo

Abstract

Intravascular optical coherence tomography (IV-OCT) is an imaging modality that can be used for the assessment of intracoronary stents. Recent publications pointed to the fact that 3D visualizations have potential advantages compared to conventional 2D representations. However, 3D imaging still requires a time consuming manual procedure not suitable for on-line application during coronary interventions. We propose an algorithm for a rapid and fully automatic 3D visualization of IV-OCT pullbacks. IV-OCT images are first processed for the segmentation of the different structures. This also allows for automatic pullback calibration. Then, according to the segmentation results, different structures are depicted with different colors to visualize the vessel wall, the stent and the guide-wire in details. Final 3D rendering results are obtained through the use of a commercial 3D DICOM viewer. Manual analysis was used as ground-truth for the validation of the segmentation algorithms. A correlation value of 0.99 and good limits of agreement (Bland Altman statistics) were found over 250 images randomly extracted from 25 in vivo pullbacks. Moreover, 3D rendering was compared to angiography, pictures of deployed stents made available by the manufacturers and to conventional 2D imaging corroborating visualization results. Computational time for the visualization of an entire data sets resulted to be ~74 sec. The proposed method allows for the on-line use of 3D IV-OCT during percutaneous coronary interventions, potentially allowing treatments optimization.

Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy

“…Therefore, the proposed algorithm can ensure that a pullback with acceptable image quality has been acquired before concluding the imaging procedure. In addition, this technique will also be helpful in post-processing techniques to accelerate three-dimensional rendering of the data sets, replacing manual analysis that is currently needed [6,17].…”

Automatic segmentation of coronary morphology using transmittance-based lumen intensity-enhanced intravascular optical coherence tomography images and applying a localized level-set-based active contour method

“…In the Maximum Intensity Projection (MIP) of a polar-format, log-compressed IV-OCT image pullback stack the guide-wire appears as a continuous feature 2,5 [see Fig. 7(a)].…”

“…7(a)]. The guide-wire segmentation is usually achieved from the MIP images by means of intensity thresholding 2 or boundary detection through a dynamic programming method. 5 As shown by the overlaid contour in Fig.…”

“…1 Automated assessment can reduce this error and produce more consistent image interpretation. Various techniques have been reported for lumen segmentation, [1][2][3][4][5][6][7][8] stent strut detection, 7 and plaque characterization. 1,9 These approaches are either semi or fully automated.…”

“…Thus, the wall-lumen interface provides a high degree of contrast and this is generally exploited by various techniques to extract the lumen boundary. Some of the methods previously employed include image thresholding, 1 A-line intensity-variation analysis, 2,3 intensity difference-dependent cost function and its minimization approach, 4,5 and labeling methods using Markov random field (MRF). 7 Alternatively, a combination approach with expectation maximization (EM) for labeling and graph-cut for lumen segmentation has been reported.…”