Automated Coronary Arteries Labeling Via Geometric Deep Learning

TL;DR

This work proposes an intuitive graph representation method, well suited to use with 3D coordinate data obtained from angiography scans, and seeks to analyze subject-specific graphs using geometric deep learning.

Abstract

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.

Figures (5)

• Figure 1: Overview of the automated coronary artery labelling framework. i) Coronary computed tomography angiography (CCTA) images from 141 subjects are annotated into 13 segments (classes) following SCCT guidelines. ii) For each CCTA image, a set of coronary artery centerlines for each segment are obtained to define the nodes, edges and node embeddings. Node features comprise spatial features to characterize the entities. iii) The coronary artery segments and branches for each subject are transformed into a graph. iv) The graph representation is processed using geometric deep learning models for node-level prediction to label the segments.
• Figure 2: Illustration of the vessel centerline used to extract features. Each centerline is composed of a list of points obtained via antiga2002patient.
• Figure 3: Graph-based representation of coronary trees segments. Segments represent branches after bifurcating. Segments are represented by multiple nodes in the graph. Visualization of 2 branches of the right coronary arteries: RCA (orange) and AM (red). The same color represents a branch, and nodes correspond to a segment
• Figure 4: Representations of specific segments, and the connections for two different subjects in the dataset (left and right boxes). The right vessel tree, which has 13 segments, is more complete, while the left tree lacks some vessel segments.
• Figure 5: Normalized confusion matrices for labeling segments (13 classes).