  Bioresorbable scaffolds have become a popular choice for treatment of
coronary heart disease, replacing traditional metal stents. Often,
intravascular optical coherence tomography is used to assess potential
malapposition after implantation and for follow-up examinations later on.
Typically, the scaffold is manually reviewed by an expert, analyzing each of
the hundreds of image slices. As this is time consuming, automatic stent
detection and visualization approaches have been proposed, mostly for metal
stent detection based on classic image processing. As bioresorbable scaffolds
are harder to detect, recent approaches have used feature extraction and
machine learning methods for automatic detection. However, these methods
require detailed, pixel-level labels in each image slice and extensive feature
engineering for the particular stent type which might limit the approaches'
generalization capabilities. Therefore, we propose a deep learning-based method
for bioresorbable scaffold visualization using only image-level labels. A
convolutional neural network is trained to predict whether an image slice
contains a metal stent, a bioresorbable scaffold, or no device. Then, we derive
local stent strut information by employing weakly supervised localization using
saliency maps with guided backpropagation. As saliency maps are generally
diffuse and noisy, we propose a novel patch-based method with image shifting
which allows for high resolution stent visualization. Our convolutional neural
network model achieves a classification accuracy of 99.0 % for image-level
stent classification which can be used for both high quality in-slice stent
visualization and 3D rendering of the stent structure.
