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@ARTICLE{Irvine:617302,
      author       = {Irvine, Sarah C. and Lucas, Christian and Bootbool, Moral
                      and Galli, Silvia and Zeller-Plumhoff, Berit and Moosmann,
                      Julian P.},
      editor       = {Müller, Bert and Wang, Ge},
      title        = {{M}ulti-modal image registration and machine learning for
                      the generation of 3{D} virtual histology of bone implants},
      journal      = {Proceedings of SPIE},
      volume       = {13152},
      issn         = {0038-7355},
      address      = {Bellingham, Wash.},
      publisher    = {SPIE},
      reportid     = {PUBDB-2024-06688},
      pages        = {70},
      year         = {2024},
      note         = {Waiting for fulltext},
      abstract     = {In our correlative characterisation studies of
                      biodegradable and permanent metal bone implants, we have
                      performed both synchrotron-radiation microtomography
                      (SR-μCT) and histology on the same samples. Histological
                      staining is still the gold standard for tissue visualisation
                      yet requires multiple time-consuming sample preparation
                      steps (fixing, embedding, sectioning and staining) before
                      imaging is performed on individual slices, in contrast to
                      the non-invasive and 3D nature of tomography. In the process
                      of correlating the corresponding data sets, we are able to
                      combine advantages of both modalities by using machine
                      learning methods to generate artificially stained 3D virtual
                      histology datasets from SR-μCT datasets. For this we have
                      developed an automated registration tool to find and fit the
                      correct virtual tomographic plane to each histology slice.
                      Preliminary results are promising after training a modified
                      cycle generative adversarial network on our data, with two
                      different histological stainings.},
      month         = {Aug},
      date          = {2024-08-18},
      organization  = {Developments in X-Ray Tomography XV,
                       San Diego (United States), 18 Aug 2024
                       - 23 Aug 2024},
      cin          = {Hereon / DOOR ; HAS-User},
      ddc          = {620},
      cid          = {I:(DE-H253)Hereon-20210428 / I:(DE-H253)HAS-User-20120731},
      pnm          = {6G3 - PETRA III (DESY) (POF4-6G3) / 05D23CG1 -
                      Verbundprojekt 05D2022 - KI4D4E: Ein KI-basiertes Framework
                      für die Visualisierung und Auswertung der massiven
                      Datenmengen der 4D-Tomographie für Endanwender von
                      Beamlines. Teilprojekt 7. (BMBF-05D23CG1)},
      pid          = {G:(DE-HGF)POF4-6G3 / G:(DE-Ds200)BMBF-05D23CG1},
      experiment   = {EXP:(DE-H253)P-P05-20150101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)16},
      doi          = {10.1117/12.3028465},
      url          = {https://bib-pubdb1.desy.de/record/617302},
}