  Coronary heart disease is a common cause of death despite being preventable.
To treat the underlying plaque deposits in the arterial walls, intravascular
optical coherence tomography can be used by experts to detect and characterize
the lesions. In clinical routine, hundreds of images are acquired for each
patient which requires automatic plaque detection for fast and accurate
decision support. So far, automatic approaches rely on classic machine learning
methods and deep learning solutions have rarely been studied. Given the success
of deep learning methods with other imaging modalities, a thorough
understanding of deep learning-based plaque detection for future clinical
decision support systems is required. We address this issue with a new dataset
consisting of in-vivo patient images labeled by three trained experts. Using
this dataset, we employ state-of-the-art deep learning models that directly
learn plaque classification from the images. For improved performance, we study
different transfer learning approaches. Furthermore, we investigate the use of
cartesian and polar image representations and employ data augmentation
techniques tailored to each representation. We fuse both representations in a
multi-path architecture for more effective feature exploitation. Last, we
address the challenge of plaque differentiation in addition to detection.
Overall, we find that our combined model performs best with an accuracy of
91.7%, a sensitivity of 90.9% and a specificity of 92.4%. Our results indicate
that building a deep learning-based clinical decision support system for plaque
detection is feasible.
