Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning

• URL: http://arxiv.org/abs/2509.17726v1
• Date: Mon, 22 Sep 2025 12:57:21 GMT
• Title: Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
• Authors: Javier Bisbal, Patrick Winter, Sebastian Jofre, Aaron Ponce, Sameer A. Ansari, Ramez Abdalla, Michael Markl, Oliver Welin Odeback, Sergio Uribe, Cristian Tejos, Julio Sotelo, Susanne Schnell, David Marlevi,
• Abstract summary: We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA)<n>Our framework offers a scalable, accurate, and uncertainty-aware solution for automated cerebrovascular labeling, supporting downstream hemodynamic analysis and facilitating clinical integration.
• Score: 2.6279333406008476
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architectures: (1) a UNet with residual encoder blocks, reflecting commonly used baselines in vascular labeling; (2) CS-Net, an attention-augmented UNet incorporating channel and spatial attention mechanisms for enhanced curvilinear structure recognition; and (3) nnUNet, a self-configuring framework that automates preprocessing, training, and architectural adaptation based on dataset characteristics. Among these, nnUNet achieved the highest labeling performance (average Dice score: 0.922; average surface distance: 0.387 mm), with improved robustness in anatomically complex vessels. To assess predictive confidence, we implemented test-time augmentation (TTA) and introduced a novel coordinate-guided strategy to reduce interpolation errors during augmented inference. The resulting uncertainty maps reliably indicated regions of anatomical ambiguity, pathological variation, or manual labeling inconsistency. We further validated clinical utility by comparing flow velocities derived from automated and manual labels in co-registered 4D Flow MRI datasets, observing close agreement with no statistically significant differences. Our framework offers a scalable, accurate, and uncertainty-aware solution for automated cerebrovascular labeling, supporting downstream hemodynamic analysis and facilitating clinical integration.

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