  Automatic labelling of anatomical structures, such as coronary arteries, is
critical for diagnosis, yet existing (non-deep learning) methods are limited by
a reliance on prior topological knowledge of the expected tree-like structures.
As the structure such vascular systems is often difficult to conceptualize,
graph-based representations have become popular due to their ability to capture
the geometric and topological properties of the morphology in an
orientation-independent and abstract manner. However, graph-based learning for
automated labeling of tree-like anatomical structures has received limited
attention in the literature. The majority of prior studies have limitations in
the entity graph construction, are dependent on topological structures, and
have limited accuracy due to the anatomical variability between subjects. In
this paper, we propose an intuitive graph representation method, well suited to
use with 3D coordinate data obtained from angiography scans. We subsequently
seek to analyze subject-specific graphs using geometric deep learning. The
proposed models leverage expert annotated labels from 141 patients to learn
representations of each coronary segment, while capturing the effects of
anatomical variability within the training data. We investigate different
variants of so-called message passing neural networks. Through extensive
evaluations, our pipeline achieves a promising weighted F1-score of 0.805 for
labeling coronary artery (13 classes) for a five-fold cross-validation.
Considering the ability of graph models in dealing with irregular data, and
their scalability for data segmentation, this work highlights the potential of
such methods to provide quantitative evidence to support the decisions of
medical experts.
