Abstract #1855

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

Javier Bisbal^1,2,3,4,5, Sebastian Jofré⁶, Patrick Winter^4,7, Aaron Ponce^2,8, Maria Aristova⁷, Jackson Moore⁷, Oliver Welin Odeback^3,4, Sameer Ansari⁷, Cristián Tejos^1,2,5, Sergio Uribe⁹, Michael Markl⁷, Julio Sotelo⁶, Susanne Schnell^4,7, and David Marlevi³

¹Biomedical Imaging Center, Pontificia Universidad Catolica de Chile, Santiago, Chile, ²Millennium Institute for Intelligent Healthcare Engineering, iHEALTH, Santiago, Chile, ³Department of molecular medicine and surgery, Karolinska Institutet, Stockholm, Sweden, ⁴Institute of Physics, University of Greifswald, Greifswald,, Germany, ⁵Department of Electrical Engineering, Pontificia Universidad Catolica de Chile, Santiago, Chile, ⁶Departamento de Informática, Universidad Técnica Federico Santa Maria, Santiago, Chile, ⁷Department of Radiology, Northwestern University, Chicago, IL, United States, ⁸Universidad de Valparaíso, Valparaíso, Chile, ⁹Department of Medical Imaging and Radiation Sciences, Monash University, Melbourne, Australia

Synopsis

Keywords: Analysis/Processing, Segmentation

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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