  Intravascular ultrasound and optical coherence tomography are widely
available for characterizing coronary stenoses and provide critical vessel
parameters to optimize percutaneous intervention. Intravascular
polarization-sensitive optical coherence tomography (PS-OCT) simultaneously
provides high-resolution cross-sectional images of vascular structures while
also revealing preponderant tissue components such as collagen and smooth
muscle and thereby enhances plaque characterization. Automated interpretation
of these features promises to facilitate the objective clinical investigation
of the natural history and significance of coronary atheromas. Here, we propose
a convolutional neural network model, optimized using a new multi-term loss
function, to classify the lumen, intima, and media layers in addition to the
guidewire and plaque shadows. We demonstrate that our multi-class
classification model outperforms state-of-the-art methods in detecting the
coronary anatomical layers. Furthermore, the proposed model segments two
classes of common imaging artifacts and detects the anatomical layers within
the thickened vessel wall regions that were excluded from analysis by other
studies. The source code and the trained model are publicly available at
https://github.com/mhaft/OCTseg
