## 2 Materials and Methods
### Reconstruction of the Stent Coronary Artery Bifurcation  
#### 2.2.1 Image Segmentation  
The three-dimensional (3D) geometry of the patient's bifurcation with the implanted Absorb BVS was reconstructed by means of a validated OCT-based methodology [6]. In particular, an automatic segmentation algorithm was implemented to detect the baseline lumen contour and the polymeric scaffold in the baseline OCT image set (Figure 1A). The algorithm comprised four main steps: (i) Image pre-preprocessing; (ii) lumen segmentation; (iii) scaffold struts segmentation; and (iv) strut-based lumen correction.  
Image pre-processing followed the strategy that was introduced by [PERSON] and colleagues [8]. Briefly, the OCT catheter and the noise inside the lumen were removed. This was achieved with a binary mask that was created through the following steps: Intensity thresholding, voxels with intensity below a threshold were neglected; morphological opening, uniform clusters of high intensity pixels were created; and area thresholding, isolated small bright regions were neglected.  
The lumen contours were segmented according to a previously described strategy [6; 9]. The pre-processed images were elaborated in polar coordinates and a Sobel filter was applied to detect edges. The first pixel detected as an edge for each image column (i.e., for each A-scan) was considered as part of the lumen contour. The identified pixels were interpolated and were converted back to Cartesian coordinates (Figure 1B).  
The scaffold struts segmentation was entirely performed in Cartesian coordinates. The criterion for scaffold identification relied on the fact that polymeric stent struts appear in the OCT images as approximately rectangular regions with a low intensity core surrounded by bright pixels. Hence, the struts were identified with three steps: (i) Intensity thresholding, by using a pre-defined quantile of the pixel intensity distribution; (ii) area filling operation, to close the holes in the binary image; (iii) Boolean subtraction of the original binary image from the filled image. When the scaffold struts presented a partially connected layer of bright pixels at their boundary (i.e., \"open box\" struts), the procedure described above failed to recognize the struts. Furthermore, false positives were identified with the implemented segmentation strategy, especially close to the outer wall.  
The problem introduced by \"open-box\" struts was addressed by an intensity thresholding step followed by a morphological closing with a disk structure element of 1-pixel radius (1 pixel is equal to \(\sim\)15 \(\upmu\)m). Furthermore, a nonlinear filter (\(\gamma\)-filter, with \(\gamma\) = 0.6) was applied to the images to enhance the image contrast. The correction of detected stent false positives comprised a series of filtering operations. Firstly, the area of acceptable struts candidates was bounded between two thresholds (e.g., 10 and 300 pixels). Secondly, the lumen was dilated using a disk of radius 20 pixels as tolerance for struts position; therefore, all the struts outside such a region were removed. Lastly, centroids of the remaining strut candidates were interpolated with a spline and stent false positives at a distance lager than 5 pixels were neglected. The resultant region of interest was composed of the actual stent struts (i.e., stent true positives) (Figure 1C).  
The strut-based lumen correction was the last step of the segmentation process. The lumen contour at baseline was obtained by combining the lumen boundary previously identified with the line that interpolates the position of the abluminal wall of the struts. This analysis was performed in polar coordinates. For each A-scan image (i.e., one-dimensional depth profile), only the contour with the highest radial value was chosen (Figure 1D).