🤖 AI Summary
This study addresses the limitations of existing brain segmentation and cortical parcellation tools when applied to ultra-high-field MRI (UHF-MRI) due to signal inhomogeneity and heterogeneity in contrast and resolution. To overcome these challenges, we propose a deep learning toolbox designed for multi-field-strength, multi-contrast, and multi-cohort scenarios, comprising two independently trained 3D U-Nets for 35-label whole-brain segmentation and 62-label Desikan–Killiany–Tourville (DKT) cortical parcellation, integrated with a volumetric measurement pipeline. Innovatively employing a domain randomization strategy trained on data from 238 subjects, our method achieves, for the first time, robust cortical parcellation on UHF-MRI. Experiments demonstrate superior performance over existing approaches across multiple heterogeneous datasets, with highly reliable parcellation and volumetric measurements that show strong agreement with standard protocols.
📝 Abstract
Despite Ultra-High Field MRI (UHF-MRI) being increasingly used in large-scale neuroimaging studies, automatic segmentation and parcellation remain challenging due to signal inhomogeneities, varying contrast and resolution, and the lack of tools optimized for UHF-MRI. Traditional software packages such as FastSurferVINN or SynthSeg+ often yield suboptimal results when applied directly to UHF images, which has limited region-based quantitative analyses. Building upon this need, we propose GOUHFI 2.0, a new implementation of GOUHFI that incorporates greater training data variation and introduces added functionalities, including cortical parcellation and volumetry. GOUHFI 2.0 preserves the contrast- and resolution-agnostic properties of the original toolbox while introducing two independently trained segmentation tasks based on the 3D U-Net architecture. The first network segments brain images of any contrast, resolution or field strength into 35 labels, using the domain randomization approach with a dataset composed of 238 subjects of varied resolutions, field strengths and populations. Using the same training dataset, the second network performs the parcellation of the cortex into 62 labels following the Desikan–Killiany–Tourville (DKT) protocol. When evaluated across multiple datasets, GOUHFI 2.0 demonstrated improved segmentation accuracy relative to the original toolbox, particularly in heterogeneous populations, and its ability to generate reliable cortical parcellations. Additionally, the added integrated volumetry pipeline enabled the derivation of results consistent with those obtained using standard volumetry procedures. In summary, GOUHFI 2.0 offers a comprehensive, contrast- and resolution-agnostic solution for brain segmentation and parcellation across field strengths. This positions GOUHFI 2.0 as a versatile tool for researchers working at UHF-MRI, making it the first Deep Learning (DL) toolbox capable of robust cortical parcellation at UHF-MRI.