Automatic Labelling of Intracranial arteries: A Comparison of UNet-based Networks

Motivation: 4D Flow MRI of intracranial arteries requires labor-intensive manual analysis for accurate hemodynamic quantification. Goal(s): This study seeks to streamline the segmentation of intracranial arteries in 4D Flow MRI to improve the speed and accuracy of cerebral blood flow analysis. Approach: Using data from 18 patients with intracranial atherosclerotic disease, we compared three UNet-based models: Residual UNet, UNETR, and nnUNet. Results: Residual UNet achieved the highest average Dice score (0.863), particularly excelling in segmenting larger arteries, in comparison with the other methods. Future work includes integrating into automated flow data analysis. Impact: This study identifies Residual UNet as an effective tool for automated intracranial artery labeling, enabling fully automatic processing of intracranial 4D Flow MRI data.

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Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/1855
Primary Topic
Brain Tumor Detection and Classification
Type
article
Field-Weighted Citation Impact
0.00
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article

Automatic Labelling of Intracranial arteries: A Comparison of UNet-based Networks

Sameer Ansari, Jackson E. Moore, Cristián Tejos, Michael Markl et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Brain Tumor Detection and Classification
article

Automatic Labelling of Intracranial arteries: A Comparison of UNet-based Networks

Sameer Ansari, Jackson E. Moore, Cristián Tejos, Michael Markl, Julio A Sotelo, Maria Aristova, Patrick M. Winter, Susanne Schnell, Javier Bisbal, Oliver Welin Odeback, Sergio Uribe, David Marlevi, Aaron Ponce, Sebastian Jofré
article en

Abstract

Motivation: 4D Flow MRI of intracranial arteries requires labor-intensive manual analysis for accurate hemodynamic quantification. Goal(s): This study seeks to streamline the segmentation of intracranial arteries in 4D Flow MRI to improve the speed and accuracy of cerebral blood flow analysis. Approach: Using data from 18 patients with intracranial atherosclerotic disease, we compared three UNet-based models: Residual UNet, UNETR, and nnUNet. Results: Residual UNet achieved the highest average Dice score (0.863), particularly excelling in segmenting larger arteries, in comparison with the other methods. Future work includes integrating into automated flow data analysis. Impact: This study identifies Residual UNet as an effective tool for automated intracranial artery labeling, enabling fully automatic processing of intracranial 4D Flow MRI data.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 69%
Brain Tumor Detection and Classification
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Automatic Labelling of Intracranial arteries: A Comparison of UNet-based Networks — Sameer Ansari, Jackson E. Moore, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2025) | TGRS Research Map | TGRS