Graph attention networks for segment labeling in coronary artery trees

Nils Hampe; Jelmer M. Wolterink; Carlos Collet; Nils Planken; Ivana Išgum

doi:10.1117/12.2581219

15 February 2021 Graph attention networks for segment labeling in coronary artery trees

Nils Hampe, Jelmer M. Wolterink, Carlos Collet, Nils Planken, Ivana Išgum

Proceedings Volume 11596, Medical Imaging 2021: Image Processing; 115961I (2021) https://doi.org/10.1117/12.2581219
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

Accurately labeled segments of the coronary artery trees are important for diagnostic reporting of coronary artery disease. As current automatic reporting tools do not consider anatomical segment labels, accurate automatic solutions for deriving these labels would be of great value. We propose an automatic method for labeling segments in coronary artery trees represented by centerlines automatically extracted from CCTA images. Using the connectivity between the centerlines, we construct a tree graph. Coronary artery segments are defined as edges of this graph and characterized by location and geometry features. The constructed coronary artery tree is transformed into a linegraph and used as input to a graph attention network, which is trained to classify labels of coronary artery segments. The method was evaluated on 71 CCTA images, achieving an F1-score of 92.4% averaged over all patients and segments. The results indicate that graph attention networks are suitable for coronary artery tree labeling.

Conference Presentation

Citation Download Citation

Nils Hampe, Jelmer M. Wolterink, Carlos Collet, Nils Planken, and Ivana Išgum "Graph attention networks for segment labeling in coronary artery trees", Proc. SPIE 11596, Medical Imaging 2021: Image Processing, 115961I (15 February 2021); https://doi.org/10.1117/12.2581219

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