% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Walter:298917,
      author       = {A. Walter$^*$ and C. Bauer$^*$ and A. K. Yawson$^*$ and P.
                      Hoegen-Saßmannshausen$^*$ and S. Adeberg and J. Debus$^*$
                      and O. Jäkel$^*$ and M. Frank and K. Giske$^*$},
      title        = {{A}ccuracy of an articulated head-and-neck motion model
                      using deep learning-based instance segmentation of skeletal
                      bones in {CT} scans for image registration in radiotherapy},
      journal      = {Computer methods in biomechanics and biomedical
                      engineering},
      volume       = {13},
      number       = {1},
      issn         = {2168-1163},
      address      = {Abingdon},
      publisher    = {Taylor $\&$ Francis},
      reportid     = {DKFZ-2025-00353},
      pages        = {2455752},
      year         = {2025},
      note         = {#EA:E040#LA:E040#},
      abstract     = {Knowing about anatomical deformations in patient images is
                      crucial for adaptive image-guided radiation therapy.
                      Biomechanical models ensure biofidelity in deformable image
                      registration, but manual contouring limits their clinical
                      use. We investigate the application of automatically
                      generated contours for a biomechanical registration model in
                      head and neck cancer treatment. For that, we automatically
                      generate individual bone segmentations on planning CT scans
                      examining a custom-trained nnU-Net model and the
                      ready-trained TotalSegmentator model. Both sets of
                      segmentations are evaluated using DICE, Hausdorff Distance
                      and surface DICE. We investigate their impact on the
                      build-up of the biomechanical articulated skeleton model by
                      deviations in joint positioning and CT-CT registration
                      accuracy using target registration error (TRE). The
                      custom-trained model achieves 1.51 ± 0.26 mm TRE, with no
                      significant difference in registration accuracy. While the
                      TotalSegmentator does not provide all structures needed for
                      the complete biomechanical model build-up. Overall, deep
                      learning–based automatic bone segmentation can replace
                      manual contouring in this model, matching its performance.},
      cin          = {E040 / E050},
      ddc          = {570},
      cid          = {I:(DE-He78)E040-20160331 / I:(DE-He78)E050-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1080/21681163.2025.2455752},
      url          = {https://inrepo02.dkfz.de/record/298917},
}