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GOUHFI 2.0: A Next-Generation Toolbox for Brain Segmentation and Cortex Parcellation at Ultra-High Field MRI.

Marc-Antoine Fortin¹, Anne Louise Kristoffersen¹, Paal Erik Goa²

¹Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway.

|February 6, 2026

Summary

GOUHFI 2.0 enhances Ultra-High Field MRI (UHF-MRI) analysis by providing robust brain segmentation and cortical parcellation. This deep learning tool improves accuracy for complex neuroimaging data.

Keywords:

Brain Segmentation Cortex Parcellation Deep Learning Domain Randomization Neuroimaging UHF-MRI Volumetry

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Area of Science:

Neuroimaging
Medical Image Analysis
Artificial Intelligence in Medicine

Background:

Ultra-High Field MRI (UHF-MRI) is vital for large-scale neuroimaging but faces challenges in automatic brain segmentation and parcellation.
Existing tools like FastSurferVINN and SynthSeg+ often produce suboptimal results on UHF data, limiting quantitative analyses.

Purpose of the Study:

To introduce GOUHFI 2.0, an advanced deep learning toolbox for improved brain segmentation and cortical parcellation specifically optimized for UHF-MRI data.
To address the limitations of current software in handling signal inhomogeneities and diverse contrasts/resolutions inherent in UHF-MRI.

Main Methods:

Developed GOUHFI 2.0 with two independently trained 3D U-Net segmentation networks using a large, diverse dataset (238 subjects) and domain randomization.
The first network performs whole-brain segmentation (35 labels), while the second performs cortical parcellation (62 labels) following the Desikan-Killiany-Tourville (DKT) protocol.
The toolbox is designed to be contrast- and resolution-agnostic, preserving the original GOUHFI's flexibility.

Main Results:

GOUHFI 2.0 demonstrated significantly improved segmentation accuracy compared to the original GOUHFI, especially in heterogeneous datasets.
The tool produced reliable cortical parcellations and consistent volumetry results, comparable to standard workflows.
It is the first deep learning toolbox to enable robust cortical parcellation specifically for UHF-MRI.

Conclusions:

GOUHFI 2.0 offers a comprehensive and robust solution for brain segmentation, parcellation, and volumetry across various field strengths in MRI.
This updated toolbox overcomes previous limitations, enabling more accurate and reliable quantitative analyses from UHF-MRI data.
It represents a significant advancement for neuroimaging research utilizing UHF-MRI, particularly for detailed cortical analysis.