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@INPROCEEDINGS{Schacht:639444,
      author       = {Schacht, Benedict and Greving, Imke and Frintrop, Simone
                      and Zeller-Plumhoff, Berit and Wilms, Christian},
      title        = {{C}ontextloss: {C}ontext {I}nformation for
                      {T}opology-{P}reserving {S}egmentation},
      address      = {[Piscataway, NJ]},
      publisher    = {IEEE},
      reportid     = {PUBDB-2025-04532},
      isbn         = {979-8-3315-2379-4},
      pages        = {1882 - 1887},
      year         = {2025},
      note         = {"This year's ICIP theme is 'Image processing in the age of
                      GenAI'" - Vorwort; Literaturangaben;},
      comment      = {[Ebook] 2025 IEEE International Conference on Image
                      Processing (ICIP) : proceedings : 14-17 September 2025,
                      Anchorage, Alaska, United States / sponsored by: the
                      Institute of Electrical and Electronics Engineers, Signal
                      Processing Society , [Piscataway, NJ] : IEEE, 2025,},
      booktitle     = {[Ebook] 2025 IEEE International
                       Conference on Image Processing (ICIP) :
                       proceedings : 14-17 September 2025,
                       Anchorage, Alaska, United States /
                       sponsored by: the Institute of
                       Electrical and Electronics Engineers,
                       Signal Processing Society ,
                       [Piscataway, NJ] : IEEE, 2025,},
      abstract     = {In image segmentation, preserving the topology of segmented
                      structures like vessels, membranes, or roads is crucial. For
                      instance, topological errors on road networks can
                      significantly impact navigation. Recently proposed solutions
                      are loss functions based on critical pixel masks that
                      consider the whole skeleton of the segmented structures in
                      the critical pixel mask. We propose the novel loss function
                      ContextLoss (CLoss) that improves topological correctness by
                      considering topological errors with their whole context in
                      the critical pixel mask. The additional context improves the
                      network focus on the topological errors. Further, we propose
                      two intuitive metrics to verify improved connectivity due to
                      a closing of missed connections. We benchmark our proposed
                      CLoss on three public datasets (2D $\&$ 3D) and our own 3D
                      nano-imaging dataset of bone cement lines. Training with our
                      proposed CLoss increases performance on topology-aware
                      metrics and repairs up to 44 $\%$ more missed connections
                      than other state-of-the-art methods. We make the code
                      publicly available1 2.},
      month         = {Sep},
      date          = {2025-09-14},
      organization  = {2025 IEEE International Conference on
                       Image Processing, Anchorage (AK), 14
                       Sep 2025 - 17 Sep 2025},
      cin          = {Hereon / DOOR ; HAS-User},
      cid          = {I:(DE-H253)Hereon-20210428 / I:(DE-H253)HAS-User-20120731},
      pnm          = {6G3 - PETRA III (DESY) (POF4-6G3) / HIDSS-0002 - DASHH:
                      Data Science in Hamburg - Helmholtz Graduate School for the
                      Structure of Matter $(2019_IVF-HIDSS-0002)$},
      pid          = {G:(DE-HGF)POF4-6G3 / $G:(DE-HGF)2019_IVF-HIDSS-0002$},
      experiment   = {EXP:(DE-H253)P-P05-20150101},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1109/ICIP55913.2025.11084563},
      url          = {https://bib-pubdb1.desy.de/record/639444},
}