  Automated anatomical labeling plays a vital role in coronary artery disease
diagnosing procedure. The main challenge in this problem is the large
individual variability inherited in human anatomy. Existing methods usually
rely on the position information and the prior knowledge of the topology of the
coronary artery tree, which may lead to unsatisfactory performance when the
main branches are confusing. Motivated by the wide application of the graph
neural network in structured data, in this paper, we propose a conditional
partial-residual graph convolutional network (CPR-GCN), which takes both
position and CT image into consideration, since CT image contains abundant
information such as branch size and spanning direction. Two majority parts, a
Partial-Residual GCN and a conditions extractor, are included in CPR-GCN. The
conditions extractor is a hybrid model containing the 3D CNN and the LSTM,
which can extract 3D spatial image features along the branches. On the
technical side, the Partial-Residual GCN takes the position features of the
branches, with the 3D spatial image features as conditions, to predict the
label for each branches. While on the mathematical side, our approach twists
the partial differential equation (PDE) into the graph modeling. A dataset with
511 subjects is collected from the clinic and annotated by two experts with a
two-phase annotation process. According to the five-fold cross-validation, our
CPR-GCN yields 95.8% meanRecall, 95.4% meanPrecision and 0.955 meanF1, which
outperforms state-of-the-art approaches.
