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@ARTICLE{Bisten:285779,
      author       = {Bisten, Justus and von Bornhaupt, Valentin and Grün,
                      Johannes and Bauer, Tobias and Rüber, Theodor and Schultz,
                      Thomas},
      title        = {{R}apid{P}arc: {A} global-context transformer for parallel,
                      accurate, and lesion-robust tractogram parcellation.},
      journal      = {Imaging neuroscience},
      volume       = {4},
      issn         = {2837-6056},
      address      = {Cambridge, MA},
      publisher    = {MIT Press},
      reportid     = {DZNE-2026-00315},
      pages        = {IMAG.a.1168},
      year         = {2026},
      abstract     = {Whole-brain diffusion MRI tractography produces
                      tractograms, dense sets of streamlines that represent white
                      matter architecture. Structural connectivity studies and
                      clinical pipelines often involve tractogram parcellation, a
                      process in which each streamline is assigned to an
                      anatomical bundle or identified as a false positive. Recent
                      advances made tractogram parcellation registration-free,
                      considering the computational effort of registration, and
                      the challenges posed by the registration of pathological
                      cases. We introduce RapidParc, a novel transformer-based
                      method for registration-free tractogram parcellation.
                      RapidParc treats each streamline as a token and processes
                      many of them in parallel. This way, they serve as a global
                      context for each other, permitting accurate classification
                      while the high level of parallelism leads to rapid
                      computation. Our design is two orders of magnitude faster
                      than TractCloud, a recent state-of-the-art method for
                      registration-free parcellation, and supports CPU-only
                      inference, while achieving even slightly higher accuracy.
                      Comparing RapidParc to TractCloud in a cohort of 22
                      individuals post-hemispherotomy and 30 individuals after
                      selective amygdalohippocampectomy (sAH), it generalizes
                      better to structurally altered anatomy, even when trained
                      exclusively on data from healthy controls. This intrinsic
                      robustness is further improved by applying a novel
                      augmentation strategy during training. Finally, we
                      investigate the main factors that contribute to that
                      improved generalization. Our results highlight the
                      importance of robust tractogram centering in
                      registration-free approaches. They also suggest that
                      constructing a local context for each streamline, on which
                      TractCloud spends considerable computational resources, does
                      not appear to contribute to its accuracy.},
      keywords     = {and lesion robustness (Other) / diffusion MRI (Other) /
                      hemispherotomy (Other) / streamline classification (Other) /
                      tractography segmentation (Other) / white matter bundles
                      (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:41878270},
      pmc          = {pmc:PMC13007388},
      doi          = {10.1162/IMAG.a.1168},
      url          = {https://pub.dzne.de/record/285779},
}