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@ARTICLE{Pollak:277719,
      author       = {Pollak, Clemens and Kügler, David and Bauer, Tobias and
                      Rüber, Theodor and Reuter, Martin},
      title        = {{F}ast{S}urfer-{LIT}: {L}esion inpainting tool for
                      whole-brain {MRI} segmentation with tumors, cavities, and
                      abnormalities.},
      journal      = {Imaging neuroscience},
      volume       = {3},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {DZNE-2025-00445},
      pages        = {$imag_a_00446$},
      year         = {2025},
      abstract     = {Resection cavities, tumors, and other lesions can
                      fundamentally alter brain structure and present as
                      abnormalities in brain MRI. Specifically, quantifying subtle
                      neuroanatomical changes in other, not directly affected
                      regions of the brain is essential to assess the impact of
                      tumors, surgery, chemo/radiotherapy, or drug treatments.
                      However, only a limited number of solutions address this
                      important task, while many standard analysis pipelines
                      simply do not support abnormal brain images at all. In this
                      paper, we present a method to perform sensitive
                      neuroanatomical analysis of healthy brain regions in the
                      presence of large lesions and cavities. Our approach called
                      'FastSurfer Lesion Inpainting Tool' (FastSurfer-LIT)
                      leverages the recently emerged Denoising Diffusion
                      Probabilistic Models (DDPM) to fill lesion areas with
                      healthy tissue that matches and extends the surrounding
                      tissue. This enables subsequent processing with established
                      MRI analysis methods such as the calculation of adjusted
                      volume and surface measurements using FastSurfer or
                      FreeSurfer. FastSurfer-LIT significantly outperforms
                      previously proposed solutions on a large dataset of
                      simulated brain tumors (N = 100) and synthetic multiple
                      sclerosis lesions (N = 39) with improved Dice and Hausdorff
                      measures, and also on a highly heterogeneous dataset with
                      lesions and cavities in a manual assessment (N = 100).
                      Finally, we demonstrate increased reliability to reproduce
                      pre-operative cortical thickness estimates from
                      corresponding post-operative temporo-mesial resection
                      surgery MRIs. The method is publicly available at
                      https://github.com/Deep-MI/LIT and will be integrated into
                      the FastSurfer toolbox.},
      keywords     = {brain filling (Other) / inpainting (Other) / lesion (Other)
                      / segmentation (Other) / software (Other) / tumor (Other)},
      cin          = {AG Reuter},
      ddc          = {610},
      cid          = {I:(DE-2719)1040310},
      pnm          = {354 - Disease Prevention and Healthy Aging (POF4-354)},
      pid          = {G:(DE-HGF)POF4-354},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40109899},
      pmc          = {pmc:PMC11917724},
      doi          = {10.1162/imag_a_00446},
      url          = {https://pub.dzne.de/record/277719},
}