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@ARTICLE{Fortin:281832,
      author       = {Fortin, Marc-Antoine and Kristoffersen, Anne Louise and
                      Larsen, Michael Staff and Lamalle, Laurent and Stirnberg,
                      Rüdiger and Goa, Pal Erik},
      title        = {{GOUHFI}: {A} novel contrast- and resolution-agnostic
                      segmentation tool for ultra-high-field {MRI}.},
      journal      = {Imaging neuroscience},
      volume       = {3},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {DZNE-2025-01213},
      pages        = {IMAG.a.960},
      year         = {2025},
      abstract     = {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.},
      keywords     = {UHF-MRI (Other) / brain segmentation (Other) / contrast and
                      resolution agnosticity (Other) / deep learning (Other) /
                      domain randomization (Other) / neuroimaging (Other)},
      cin          = {AG Stöcker},
      ddc          = {610},
      cid          = {I:(DE-2719)1013026},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:41158555},
      pmc          = {pmc:PMC12556684},
      doi          = {10.1162/IMAG.a.960},
      url          = {https://pub.dzne.de/record/281832},
}