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@ARTICLE{Upschulte:906623,
      author       = {Upschulte, Eric and Harmeling, Stefan and Amunts, Katrin
                      and Dickscheid, Timo},
      title        = {{C}ontour proposal networks for biomedical instance
                      segmentation},
      journal      = {Medical image analysis},
      volume       = {77},
      issn         = {1361-8415},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-01559},
      pages        = {102371 -},
      year         = {2022},
      abstract     = {We present a conceptually simple framework for object
                      instance segmentation, called Contour Proposal Network
                      (CPN), which detects possibly overlapping objects in an
                      image while simultaneously fitting closed object contours
                      using a fixed-size representation based on Fourier
                      Descriptors. The CPN can incorporate state-of-the-art object
                      detection architectures as backbone networks into a
                      single-stage instance segmentation model that can be trained
                      end-to-end. We construct CPN models with different backbone
                      networks and apply them to instance segmentation of cells in
                      datasets from different modalities. In our experiments, CPNs
                      outperform U-Net, Mask R-CNN and StarDist in instance
                      segmentation accuracy. We present variants with execution
                      times suitable for real-time applications. The trained
                      models generalize well across different domains of cell
                      types. Since the main assumption of the framework is closed
                      object contours, it is applicable to a wide range of
                      detection problems also beyond the biomedical domain. An
                      implementation of the model architecture in PyTorch is
                      freely available.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HIBALL - Helmholtz International BigBrain Analytics and
                      Learning Laboratory (HIBALL) (InterLabs-0015) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539) /
                      DFG project 347572269 - Heterogenität von Zytoarchitektur,
                      Chemoarchitektur und Konnektivität in einem großskaligen
                      Computermodell der menschlichen Großhirnrinde (347572269) /
                      Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(DE-HGF)InterLabs-0015 /
                      G:(EU-Grant)945539 / G:(GEPRIS)347572269 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35180674},
      UT           = {WOS:000912927000003},
      doi          = {10.1016/j.media.2022.102371},
      url          = {https://juser.fz-juelich.de/record/906623},
}