  The presence of plaques in the coronary arteries is a major risk to the
patients' life. In particular, non-calcified plaques pose a great challenge, as
they are harder to detect and more likely to rupture than calcified plaques.
While current deep learning techniques allow precise segmentation of real-life
images, the performance in medical images is still low. This is caused mostly
by blurriness and ambiguous voxel intensities of unrelated parts that fall on
the same value range. In this paper, we propose a novel methodology for
segmenting calcified and non-calcified plaques in CCTA-CPR scans of coronary
arteries. The input slices are masked so only the voxels within the wall vessel
are considered for segmentation, thus, reducing ambiguity. This mask can be
automatically generated via a deep learning-based vessel detector, that
provides not only the contour of the outer artery wall, but also the inner
contour. For evaluation, we utilized a dataset in which each voxel is carefully
annotated as one of five classes: background, lumen, artery wall, calcified
plaque, or non-calcified plaque. We also provide an exhaustive evaluation by
applying different types of masks, in order to validate the potential of vessel
masking for plaque segmentation. Our methodology results in a prominent boost
in segmentation performance, in both quantitative and qualitative evaluation,
achieving accurate plaque shapes even for the challenging non-calcified
plaques. Furthermore, when using highly accurate masks, difficult cases such as
stenosis become segmentable. We believe our findings can lead the future
research for high-performance plaque segmentation.
