% 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”.

@INPROCEEDINGS{Upschulte:1007216,
      author       = {Upschulte, Eric and Harmeling, Stefan and Amunts, Katrin
                      and Dickscheid, Timo},
      title        = {{U}ncertainty-{A}ware {C}ontour {P}roposal {N}etworks for
                      {C}ell {S}egmentation in {M}ulti-{M}odality
                      {H}igh-{R}esolution {M}icroscopy {I}mages},
      reportid     = {FZJ-2023-01988},
      pages        = {12},
      year         = {2022},
      comment      = {Proceedings of the NeurIPS CellSeg 2022 Challenge},
      booktitle     = {Proceedings of the NeurIPS CellSeg
                       2022 Challenge},
      abstract     = {We present a simple framework for cell segmentation, based
                      on uncertainty-aware Contour Proposal Networks (CPNs). It is
                      designed to provide high segmentation accuracy while
                      remaining computationally efficient, which makes it an ideal
                      solution for high throughput microscopy applications. Each
                      predicted cell is provided with four uncertainty estimations
                      that give information about the localization accuracy of the
                      detected cell boundaries. Such additional insights are
                      valuable for downstream single-cell analysis in microscopy
                      image-based biology and biomedical research. In the context
                      of the NeurIPS 22 Cell Segmentation Challenge, the proposed
                      solution is shown to generalize well in a multi-modality
                      setting, while respecting domain-specific requirements such
                      as focusing on specific cell types. Without an ensemble or
                      test-time augmentation the method achieves an F1 score of
                      0.8986 on the challenge's validation set.Code is available
                      at https://github.com/FZJ-INM1-BDA/neurips22-cell-seg.},
      month         = {Dec},
      date          = {2022-12-06},
      organization  = {NeurIPS 2022 Weakly Supervised Cell
                       Segmentation in Multi-modality
                       High-Resolution Microscopy Images, New
                       Orleans (USA), 6 Dec 2022 - 6 Dec 2022},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / DFG project 313856816 - SPP 2041: Computational
                      Connectomics (313856816) / Helmholtz AI - Helmholtz
                      Artificial Intelligence Coordination Unit – Local Unit FZJ
                      (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(EU-Grant)945539 /
                      G:(DE-HGF)InterLabs-0015 / G:(GEPRIS)313856816 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      url          = {https://juser.fz-juelich.de/record/1007216},
}