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@ARTICLE{LopesMarinho:616536,
      author       = {Lopes Marinho, André and Kazimi, Bashir and Ćwieka, Hanna
                      and Marek, Romy and Beckmann, Felix and Willumeit-Römer,
                      Regine and Moosmann, Julian and Zeller-Plumhoff, Berit},
      title        = {{A} comparison of deep learning segmentation models for
                      synchrotron radiation based tomograms of biodegradable bone
                      implants},
      journal      = {Frontiers in physics},
      volume       = {12},
      issn         = {2296-424X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {PUBDB-2024-06448},
      pages        = {1257512},
      year         = {2024},
      abstract     = {ntroduction: Synchrotron radiation micro-computed
                      tomography (SRμCT) has been used as a non-invasive
                      technique to examine the microstructure and tissue
                      integration of biodegradable bone implants. To be able to
                      characterize parameters regarding the disintegration and
                      osseointegration of such materials quantitatively, the
                      three-dimensional (3D) image data provided by SRμCT needs
                      to be processed by means of semantic segmentation. However,
                      accurate image segmentation is challenging using traditional
                      automated techniques. This study investigates the
                      effectiveness of deep learning approaches for semantic
                      segmentation of SRμCT volumes of Mg-based implants in sheep
                      bone ex vivo.Methodology: For this purpose different
                      convolutional neural networks (CNNs), including U-Net,
                      HR-Net, U²-Net, from the TomoSeg framework, the Scaled
                      U-Net framework, and 2D/3D U-Net from the nnU-Net framework
                      were trained and validated. The image data used in this work
                      was part of a previous study where biodegradable screws were
                      surgically implanted in sheep tibiae and imaged using SRμCT
                      after different healing periods. The comparative analysis of
                      CNN models considers their performance in semantic
                      segmentation and subsequent calculation of degradation and
                      osseointegration parameters. The models’ performance is
                      evaluated using the intersection over union (IoU) metric,
                      and their generalization ability is tested on unseen
                      datasets.Results and discussion: This work shows that the 2D
                      nnU-Net achieves better generalization performance, with the
                      degradation layer being the most challenging label to
                      segment for all models.},
      cin          = {Hereon / DOOR ; HAS-User},
      ddc          = {530},
      cid          = {I:(DE-H253)Hereon-20210428 / I:(DE-H253)HAS-User-20120731},
      pnm          = {6G3 - PETRA III (DESY) (POF4-6G3) / FS-Proposal: I-20200278
                      (I-20200278) / FS-Proposal: I-20191467 (I-20191467)},
      pid          = {G:(DE-HGF)POF4-6G3 / G:(DE-H253)I-20200278 /
                      G:(DE-H253)I-20191467},
      experiment   = {EXP:(DE-H253)P-P05-20150101 / EXP:(DE-H253)P-P07-20150101},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001156897500001},
      doi          = {10.3389/fphy.2024.1257512},
      url          = {https://bib-pubdb1.desy.de/record/616536},
}