Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866911539522437120 |
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| author | Bisbal, Javier Winter, Patrick Jofre, Sebastian Ponce, Aaron Ansari, Sameer A. Abdalla, Ramez Markl, Michael Odeback, Oliver Welin Uribe, Sergio Tejos, Cristian Sotelo, Julio Schnell, Susanne Marlevi, David |
| author_facet | Bisbal, Javier Winter, Patrick Jofre, Sebastian Ponce, Aaron Ansari, Sameer A. Abdalla, Ramez Markl, Michael Odeback, Oliver Welin Uribe, Sergio Tejos, Cristian Sotelo, Julio Schnell, Susanne Marlevi, David |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_17726 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning Bisbal, Javier Winter, Patrick Jofre, Sebastian Ponce, Aaron Ansari, Sameer A. Abdalla, Ramez Markl, Michael Odeback, Oliver Welin Uribe, Sergio Tejos, Cristian Sotelo, Julio Schnell, Susanne Marlevi, David Computer Vision and Pattern Recognition Machine Learning I.4.0 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. |
| title | Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning |
| topic | Computer Vision and Pattern Recognition Machine Learning I.4.0 |
| url | https://arxiv.org/abs/2509.17726 |