281832 20251114102932.0 10.1162/IMAG.a.960 doi pmid:41158555 pmid pmc:PMC12556684 pmc DZNE-2025-01213 English 610 Fortin, Marc-Antoine 0009-0006-9724-0626 0 GOUHFI: A novel contrast- and resolution-agnostic segmentation tool for ultra-high-field MRI. Cambridge, MA 2025 MIT Press article DRIVER Output Types/Journal article DataCite Journal Article journal journal PUB:(DE-HGF)16 1763035017_32675 PUB:(DE-HGF) ARTICLE BibTeX JOURNAL_ARTICLE ORCID Journal Article 0 EndNote Recently, ultra-high-field MRI (UHF-MRI) has become more available and one of the best tools to study the brain for neuroscientists. One common step in quantitative neuroimaging is to segment the brain into several regions, which has been done using software packages such as FreeSurfer, FastSurferVINN, or SynthSeg. However, the differences between UHF-MRI and 1.5T or 3T images are such that the automatic segmentation techniques optimized at these field strengths usually produce unsatisfactory segmentation results for UHF images. Thus, it has been particularly challenging to perform region-based quantitative analyses as typically done with 1.5-3T data, considerably limiting the potential of UHF-MRI until now. Ultimately, this underscores the crucial need for developing new automatic segmentation techniques designed to handle UHF images. Hence, we propose a novel Deep Learning (DL)-based segmentation technique called GOUHFI: Generalized and Optimized segmentation tool for ultra-high-field images, designed to segment UHF images of various contrasts and resolutions. For training, we used a total of 206 label maps from four datasets acquired at 3T, 7T, and 9.4T. In contrast to most DL strategies, we used a previously proposed domain randomization approach, where synthetic images generated from the 206 label maps were used for training a 3D U-Net. This approach enables the DL model to become contrast agnostic. GOUHFI was tested on seven different datasets and compared with existing techniques such as FastSurferVINN, SynthSeg, and CEREBRUM-7T. GOUHFI was able to segment the six contrasts and seven resolutions tested at 3T, 7T, and 9.4T. Average Dice-Sørensen Similarity Coefficient (DSC) scores of 0.90, 0.90, and 0.93 were computed against the ground truth segmentations at 3T, 7T, and 9.4T, respectively. These results demonstrated GOUHFI's superior performance to competing approaches at each resolution and contrast level tested. Moreover, GOUHFI demonstrated impressive resistance to the typical inhomogeneities observed at UHF-MRI, making it a new powerful segmentation tool allowing the usual quantitative analysis pipelines performed at lower fields to be applied also at UHF. Ultimately, GOUHFI is a promising new segmentation tool, being the first of its kind proposing a contrast- and resolution-agnostic alternative for UHF-MRI without requiring fine tuning or retraining, making it the forthcoming alternative for neuroscientists working with UHF-MRI or even lower field strengths. 354 - Disease Prevention and Healthy Aging (POF4-354) G:(DE-HGF)POF4-354 POF4-354 POF IV 0 Dataset connected to CrossRef, PubMed, , Journals: pub.dzne.de UHF-MRI Other brain segmentation Other contrast and resolution agnosticity Other deep learning Other domain randomization Other neuroimaging Other Kristoffersen, Anne Louise 1 Larsen, Michael Staff 2 Lamalle, Laurent 3 Stirnberg, Rüdiger P:(DE-2719)2810697 4 dzne Goa, Pal Erik P:(DE-2719)9002873 5 dzne 10.1162/IMAG.a.960 Vol. 3, p. IMAG.a.960 PERI:(DE-600)3167925-0 IMAG.a.960 Imaging neuroscience 3 2025 2837-6056 OpenAccess http://pub.dzne.de/record/281832/files/DZNE-2025-01213.pdf OpenAccess pdfa http://pub.dzne.de/record/281832/files/DZNE-2025-01213.pdf?subformat=pdfa oai:pub.dzne.de:281832 openaire open_access VDB driver dnbdelivery Deutsches Zentrum für Neurodegenerative Erkrankungen I:(DE-588)1065079516 DZNE 4 P:(DE-2719)2810697 External Institute I:(DE-HGF)0 Extern 5 P:(DE-2719)9002873 DE-HGF Gesundheit Neurodegenerative Diseases G:(DE-HGF)POF4-350 G:(DE-HGF)POF4-354 G:(DE-HGF)POF4 G:(DE-HGF)POF4-300 G:(DE-HGF)POF Disease Prevention and Healthy Aging 0 2025 Creative Commons Attribution CC BY 4.0 LIC:(DE-HGF)CCBY4 HGFVOC DBCoverage StatID:(DE-HGF)0501 StatID DOAJ Seal 2024-09-26T09:40:26Z DBCoverage StatID:(DE-HGF)0500 StatID DOAJ 2024-09-26T09:40:26Z OpenAccess StatID:(DE-HGF)0510 StatID Peer Review StatID:(DE-HGF)0030 StatID DOAJ : Anonymous peer review 2024-09-26T09:40:26Z Article Processing Charges StatID:(DE-HGF)0561 StatID 2025-01-02 DBCoverage StatID:(DE-HGF)0300 StatID Medline 2025-01-02 Fees StatID:(DE-HGF)0700 StatID 2025-01-02 I:(DE-2719)1013026 AG Stöcker MR Physics 0 journal VDB UNRESTRICTED I:(DE-2719)1013026 FullTexts