Graph convolutional networks for automated intracranial artery labeling

Iris N. Vos; Ynte M. Ruigrok; Ishaan R. Bhat; Kimberley M. Timmins; Birgitta K. Velthuis; Hugo J. Kuijf

doi:10.1117/1.JMI.11.1.014007

15 February 2024 Graph convolutional networks for automated intracranial artery labeling

Iris N. Vos, Ynte M. Ruigrok, Ishaan R. Bhat, Kimberley M. Timmins, Birgitta K. Velthuis, Hugo J. Kuijf

Author Affiliations +

Journal of Medical Imaging, Vol. 11, Issue 1, 014007 (February 2024). https://doi.org/10.1117/1.JMI.11.1.014007

Abstract

Purpose

Unruptured intracranial aneurysms (UIAs) can cause aneurysmal subarachnoid hemorrhage, a severe and often lethal type of stroke. Automated labeling of intracranial arteries can facilitate the identification of risk factors associated with UIAs. This study aims to improve intracranial artery labeling using atlas-based features in graph convolutional networks.

Approach

We included three-dimensional time-of-flight magnetic resonance angiography scans from 150 individuals. Two widely used graph convolutional operators, GCNConv and GraphConv, were employed in models trained to classify 12 bifurcations of interest. Cross-validation was applied to explore the effectiveness of atlas-based features in node classification. The results were tested for statistically significant differences using a Wilcoxon signed-rank test. Model repeatability and calibration were assessed on the test set for both operators. In addition, we evaluated model interpretability and node feature contribution using explainable artificial intelligence.

Results

Atlas-based features led to statistically significant improvements in node classification (p<0.05). The results showed that the best discrimination and calibration performances were obtained using the GraphConv operator, which yielded a mean recall of 0.87, precision of 0.90, and expected calibration error of 0.02.

Conclusions

The addition of atlas-based features improved node classification results. The GraphConv operator, which incorporates higher-order structural information during training, is recommended over the GCNConv operator based on the accuracy and calibration of predicted outcomes.

Citation Download Citation

Iris N. Vos, Ynte M. Ruigrok, Ishaan R. Bhat, Kimberley M. Timmins, Birgitta K. Velthuis, and Hugo J. Kuijf "Graph convolutional networks for automated intracranial artery labeling," Journal of Medical Imaging 11(1), 014007 (15 February 2024). https://doi.org/10.1117/1.JMI.11.1.014007

Received: 15 March 2023; Accepted: 22 January 2024; Published: 15 February 2024

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KEYWORDS

Arteries

Calibration

Education and training

Performance modeling

Independent component analysis

Spherical lenses

Feature extraction

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