Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography

Nils Hampe; Sanne G. M. van Velzen; Jelmer M. Wolterink; Carlos Collet; José P. S. Henriques; Nils Planken; Ivana Išgum

doi:10.1117/1.JMI.11.3.034001

15 May 2024 Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography

Nils Hampe, Sanne G. M. van Velzen, Jelmer M. Wolterink, Carlos Collet, José P. S. Henriques, Nils Planken, Ivana Išgum

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 3, 034001 (May 2024). https://doi.org/10.1117/1.JMI.11.3.034001

Abstract

Purpose

Automatic comprehensive reporting of coronary artery disease (CAD) requires anatomical localization of the coronary artery pathologies. To address this, we propose a fully automatic method for extraction and anatomical labeling of the coronary artery tree using deep learning.

Approach

We include coronary CT angiography (CCTA) scans of 104 patients from two hospitals. Reference annotations of coronary artery tree centerlines and labels of coronary artery segments were assigned to 10 segment classes following the American Heart Association guidelines. Our automatic method first extracts the coronary artery tree from CCTA, automatically placing a large number of seed points and simultaneous tracking of vessel-like structures from these points. Thereafter, the extracted tree is refined to retain coronary arteries only, which are subsequently labeled with a multi-resolution ensemble of graph convolutional neural networks that combine geometrical and image intensity information from adjacent segments.

Results

The method is evaluated on its ability to extract the coronary tree and to label its segments, by comparing the automatically derived and the reference labels. A separate assessment of tree extraction yielded an F1 score of 0.85. Evaluation of our combined method leads to an average F1 score of 0.74.

Conclusions

The results demonstrate that our method enables fully automatic extraction and anatomical labeling of coronary artery trees from CCTA scans. Therefore, it has the potential to facilitate detailed automatic reporting of CAD.

Citation Download Citation

Nils Hampe, Sanne G. M. van Velzen, Jelmer M. Wolterink, Carlos Collet, José P. S. Henriques, Nils Planken, and Ivana Išgum "Graph neural networks for automatic extraction and labeling of the coronary artery tree in CT angiography," Journal of Medical Imaging 11(3), 034001 (15 May 2024). https://doi.org/10.1117/1.JMI.11.3.034001

Received: 14 July 2023; Accepted: 26 April 2024; Published: 15 May 2024

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KEYWORDS

Arteries

Anatomy

Image segmentation

Education and training

Neural networks

Machine learning

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