  The segmentation and analysis of coronary arteries from intravascular optical
coherence tomography (IVOCT) is an important aspect of diagnosing and managing
coronary artery disease. Current image processing methods are hindered by the
time needed to generate expert-labelled datasets and the potential for bias
during the analysis. Therefore, automated, robust, unbiased and timely geometry
extraction from IVOCT, using image processing, would be beneficial to
clinicians. With clinical application in mind, we aim to develop a model with a
small memory footprint that is fast at inference time without sacrificing
segmentation quality. Using a large IVOCT dataset of 12,011 expert-labelled
images from 22 patients, we construct a new deep learning method based on
capsules which automatically produces lumen segmentations. Our dataset contains
images with both blood and light artefacts (22.8%), as well as metallic (23.1%)
and bioresorbable stents (2.5%). We split the dataset into a training (70%),
validation (20%) and test (10%) set and rigorously investigate design
variations with respect to upsampling regimes and input selection. We show that
our developments lead to a model, DeepCap, that is on par with state-of-the-art
machine learning methods in terms of segmentation quality and robustness, while
using as little as 12% of the parameters. This enables DeepCap to have per
image inference times up to 70% faster on GPU and up to 95% faster on CPU
compared to other state-of-the-art models. DeepCap is a robust automated
segmentation tool that can aid clinicians to extract unbiased geometrical data
from IVOCT.
