Coronary Artery Segmentation from Intravascular Optical Coherence Tomography Using Deep Capsules

The segmentation and analysis of coronary arteries from intravascular optical coherence tomography (IVOCT) is an important aspect of diagnosing and managing coronary artery disease. Automated, robust, and timely geometry extraction from IVOCT, using image processing, would be beneficial to clinicians as modern biomechanical analysis relies on these geometries. Current image processing methods are hindered by the time needed to generate these expert-labelled datasets and the potential for bias during the analysis. Here we present a new deep learning method based on capsules to automatically produce lumen segmentations, built using a large IVOCT dataset of 12,011 images with ground-truth segmentations. With clinical application in mind, our model aims to have a small memory footprint and be fast at inference time without sacrificing segmentation quality. 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 also show that our model outperforms a UNet-ResNet-18 on a test set, with a better soft Dice score, pixel sensitivity and specificity, while only taking up 19% of the disk space and being 39% faster during CPU inference. Finally, we show that our fully trained and optimized model achieves a mean soft Dice score of 97.31% (median of 98.22%) on a test set.