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@ARTICLE{Estrada:267481,
      author       = {Estrada, Santiago and Kügler, David and Bahrami Rad, Emad
                      and Xu, Peng and Mousa, Dilshad and Breteler, Monique M. B.
                      and Aziz, N. Ahmad and Reuter, Martin},
      title        = {{F}ast{S}urfer-{H}yp{VINN}: {A}utomated sub-segmentation of
                      the hypothalamus and adjacent structures on
                      high-resolutional brain {MRI}},
      journal      = {Imaging neuroscience},
      volume       = {1},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {DZNE-2024-00132},
      pages        = {1 - 32},
      year         = {2023},
      abstract     = {The hypothalamus plays a crucial role in the regulation of
                      a broad range of physiological, behavioral, and cognitive
                      functions. However, despite its importance, only a few
                      small-scale neuroimaging studies have investigated its
                      substructures, likely due to the lack of fully automated
                      segmentation tools to address scalability and
                      reproducibility issues of manual segmentation. While the
                      only previous attempt to automatically sub-segment the
                      hypothalamus with a neural network showed promise for 1.0 mm
                      isotropic T1-weighted (T1w) magnetic resonance imaging
                      (MRI), there is a need for an automated tool to sub-segment
                      also high-resolutional (HiRes) MR scans, as they are
                      becoming widely available, and include structural detail
                      also from multi-modal MRI. We, therefore, introduce a novel,
                      fast, and fully automated deep-learning method named HypVINN
                      for sub-segmentation of the hypothalamus and adjacent
                      structures on 0.8 mm isotropic T1w and T2w brain MR images
                      that is robust to missing modalities. We extensively
                      validate our model with respect to segmentation accuracy,
                      generalizability, in-session test-retest reliability, and
                      sensitivity to replicate hypothalamic volume effects (e.g.,
                      sex differences). The proposed method exhibits high
                      segmentation performance both for standalone T1w images as
                      well as for T1w/T2w image pairs. Even with the additional
                      capability to accept flexible inputs, our model matches or
                      exceeds the performance of state-of-the-art methods with
                      fixed inputs. We, further, demonstrate the generalizability
                      of our method in experiments with 1.0 mm MR scans from both
                      the Rhineland Study and the UK Biobank-an independent
                      dataset never encountered during training with different
                      acquisition parameters and demographics. Finally, HypVINN
                      can perform the segmentation in less than a minute
                      (graphical processing unit [GPU]) and will be available in
                      the open source FastSurfer neuroimaging software suite,
                      offering a validated, efficient, and scalable solution for
                      evaluating imaging-derived phenotypes of the hypothalamus.},
      cin          = {AG Reuter / AG Aziz / AG Breteler},
      ddc          = {050},
      cid          = {I:(DE-2719)1040310 / I:(DE-2719)5000071 /
                      I:(DE-2719)1012001},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      experiment   = {EXP:(DE-2719)Rhineland Study-20190321},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39574480},
      pmc          = {pmc:PMC11576934},
      doi          = {10.1162/imag_a_00034},
      url          = {https://pub.dzne.de/record/267481},
}