Fig 1.
Examples of stented coronary bifurcation phantoms: A) Bifurcation phantom with a bifurcation angle of 40° and a Resolute Integrity stent (Case 1). B) Detail of the Resolute Integrity stent implanted in a 40° bifurcation phantom (Case 1). C) Detail of the Xience Prime stent implanted in a 40° bifurcation phantom (Case 2).
Table 1.
Main characteristics of the coronary bifurcation phantoms, implanted stent types, and stenting procedures followed by interventional cardiologists for stent implantation.
Fig 2.
Pre-processing steps: A) Original RGB OCT image. B) Greyscale image. C) Image after crop of the lower part, which represents the longitudinal view of the vessel phantom. D) Image without visualization tools and catheter.
Fig 3.
Lumen contour detection steps. A) Pre-processed image (in polar coordinates). The red line highlights an example of A-scan. B) Image without background noise. C) Raw lumen contour detection. D) Detected lumen contour (green) and validity region of the segmentation (purple). E) Lumen contour without misdetections. F) Lumen contour (blue) detected after gaps closing, smoothing, and conversion back to Cartesian coordinates. The polar coordinate system (r; θ) or the Cartesian coordinate system (i; j) is indicated on the top left of each image.
Fig 4.
Example of stent strut detection. A) Two A-scans are analyzed. The first one passes through a stent strut while the second one passes only through the vessel wall. The polar coordinate system (r; θ) is indicated on the top left. B) Corresponding intensity profiles of A-scans 1 and 2. The strut is detected because of the higher slope of the intensity profile of its A-scan.
Fig 5.
Stent struts detection algorithm steps. A) Pre-processed image (in polar coordinates). B) Rough detection. C) Result of the application of the triangular shaped window followed by an intensity thresholding. D) Detected struts (green) and the validity region of the segmentation (purple). E) Image without errors. F) Detected struts (purple) overlapped to the original image (green) in Cartesian coordinates. The polar coordinate system (r; θ) or the Cartesian coordinate system (i; j) is indicated on the top left of each image.
Fig 6.
A) Three-dimensional point cloud of the main branch of a bifurcation phantom with an implanted Resolute Integrity stent (case 1) obtained with the lumen border and stent struts detection algorithms. B, C) Details of the stent point cloud.
Table 2.
Percentiles of the distributions of lumen areas and distance between the lumen contours obtained with the automatic and manual segmentation methods.
Fig 7.
Top—Linear regression plots of the lumen area of 160 randomly selected OCT images: A) automatic segmentation against manual segmentation by image reader 1 (R1); B) automatic segmentation against manual segmentation by image reader 2 (R2); C) manual segmentation by R1 against that by R2. Bottom—Bland-Altman plots of the lumen area: D) automatic segmentation against R1; E) automatic segmentation against R2; F) manual segmentation by R1 against R2.
Table 3.
Similarity indexes of lumen and stent strut detection algorithms.
Fig 8.
Distribution of the distance between the lumen contours obtained on 160 randomly selected OCT images with (A) the automatic algorithm and manual segmentation by image reader 1, (B) the automatic algorithm and manual segmentation by image reader 2, and (C) the two manual segmentations.
Table 4.
Percentiles of the distributions of number of struts obtained with the automatic and manual segmentation methods, and total and radial distances between the centroid of each automatically segmented strut and the nearest manually identified strut.
Fig 9.
Distributions of the total (top) and radial (bottom) distances between the centroid of each segmented strut (A, D) by the automatic algorithm and the nearest manually identified strut by image reader 1, (B, E) by the automatic algorithm and the nearest manually identified strut by image reader 2, and (C, F) by the two manual segmentations.
Fig 10.
Bland-Altman diagrams of length of apposition (LOA): A) automatic segmentation against R1; B) automatic segmentation against R2; C) manual segmentation by R1 against R2.
Fig 11.
Superimposition of the stent point clouds obtained through the automatic detection algorithm (red) and micro-CT (black): A) Case 1 (Resolute Integrity 3x18 mm). B) Case 2 (Xience Prime 3x28 mm).
Fig 12.
Distributions of the total (top) and radial (bottom) distances between corresponding points of the stents: A, C) Case 1 (Resolute Integrity 3x18 mm). B, D) Case 2 (Xience Prime 3x28 mm).
Table 5.
Lumen volume and the mean number of struts per frame obtained for the seven repetitions of the OCT scan of Case 3.
Fig 13.
Three-dimensional lumen and stent point clouds of the four patient-specific stented coronary segments under investigation, which were obtained by applying the developed lumen border and stent struts detection algorithms: A) distal right coronary artery segment treated with Xience Prime 3.5x28 mm; B) mid right coronary artery segment treated with Xience Prime 3.5x28 mm; C) left anterior descending coronary artery segment treated with Resolute Integrity 3.5x18 mm; D) left anterior descending coronary artery segment treated with Resolute Integrity 2.75x14 mm. For each case, details of the stent point cloud are provided.