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@ARTICLE{Ma:1015298,
      author       = {Ma, Jun and Xie, Ronald and Ayyadhury, Shamini and Ge,
                      Cheng and Gupta, Anubha and Gupta, Ritu and Gu, Song and
                      Zhang, Yao and Lee, Gihun and Kim, Joonkee and Lou, Wei and
                      Li, Haofeng and Upschulte, Eric and Dickscheid, Timo and de
                      Almeida, José Guilherme and Wang, Yixin and Han, Lin and
                      Yang, Xin and Labagnara, Marco and Rahi, Sahand Jamal and
                      Kempster, Carly and Pollitt, Alice and Espinosa, Leon and
                      Mignot, Tâm and Middeke, Jan Moritz and Eckardt, Jan-Niklas
                      and Li, Wangkai and Li, Zhaoyang and Cai, Xiaochen and Bai,
                      Bizhe and Greenwald, Noah F. and Van Valen, David and
                      Weisbart, Erin and Cimini, Beth A. and Li, Zhuoshi and Zuo,
                      Chao and Brück, Oscar and Bader, Gary D. and Wang, Bo},
      title        = {{T}he {M}ulti-modality {C}ell {S}egmentation {C}hallenge:
                      {T}owards {U}niversal {S}olutions},
      journal      = {arxiv},
      publisher    = {arXiv},
      reportid     = {FZJ-2023-03643},
      year         = {2023},
      abstract     = {Cell segmentation is a critical step for quantitative
                      single-cell analysis in microscopy images. Existing cell
                      segmentation methods are often tailored to specific
                      modalities or require manual interventions to specify
                      hyperparameters in different experimental settings. Here, we
                      present a multi-modality cell segmentation benchmark,
                      comprising over 1500 labeled images derived from more than
                      50 diverse biological experiments. The top participants
                      developed a Transformer-based deep-learning algorithm that
                      not only exceeds existing methods, but can also be applied
                      to diverse microscopy images across imaging platforms and
                      tissue types without manual parameter adjustments. This
                      benchmark and the improved algorithm offer promising avenues
                      for more accurate and versatile cell analysis in microscopy
                      imaging.},
      keywords     = {Image and Video Processing (eess.IV) (Other) / Computer
                      Vision and Pattern Recognition (cs.CV) (Other) / Machine
                      Learning (cs.LG) (Other) / Quantitative Methods (q-bio.QM)
                      (Other) / FOS: Electrical engineering, electronic
                      engineering, information engineering (Other) / FOS: Computer
                      and information sciences (Other) / FOS: Biological sciences
                      (Other)},
      cin          = {INM-1},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
                      DFG project 347572269 - Heterogenität von Zytoarchitektur,
                      Chemoarchitektur und Konnektivität in einem großskaligen
                      Computermodell der menschlichen Großhirnrinde (347572269) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / HIBALL - Helmholtz International BigBrain
                      Analytics and Learning Laboratory (HIBALL) (InterLabs-0015)
                      / Helmholtz AI - Helmholtz Artificial Intelligence
                      Coordination Unit – Local Unit FZJ (E.40401.62)},
      pid          = {G:(DE-HGF)POF4-5254 / G:(GEPRIS)347572269 /
                      G:(EU-Grant)945539 / G:(DE-HGF)InterLabs-0015 /
                      G:(DE-Juel-1)E.40401.62},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.48550/arXiv.2308.05864},
      url          = {https://juser.fz-juelich.de/record/1015298},
}