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Published February 27, 2024 | Version v1
Dataset Open

NeurIPS 2022 Cell Segmentation Competition Dataset

Creators

  • 1. ROR icon University of Toronto
  • 2. Ocean University of China
  • 3. Indraprastha Institute of Information Technology Delhi (IIITD)
  • 4. Dr. BRA-IRCH, All India Institute of Medical Sciences, New Delhi
  • 5. Nanjing Anke Medical Technology CO., LTD
  • 6. Shanghai AI Laboratory
  • 7. KAIST
  • 8. ROR icon Korea Advanced Institute of Science and Technology
  • 9. The Chinese University of Hongkong (Shenzhen)
  • 10. ROR icon Shenzhen Research Institute of Big Data
  • 11. Research Centre Jülich Research Centre Jülich
  • 12. Research Centre Jülich
  • 13. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI)
  • 14. Stanford University
  • 15. New York University
  • 16. Shenzhen University
  • 17. EPFL
  • 18. ROR icon University of Reading
  • 19. CNRS–Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée
  • 20. Universitätsklinikum Dresden
  • 21. University of Science and Technology of China
  • 22. Nanjing University
  • 23. The University of Queensland
  • 24. Caltech
  • 25. Broad Institute of MIT and Harvard
  • 26. University of Waterloo
  • 27. Helsinki University Hospital

Description

The official data set for the NeurIPS 2022 competition: cell segmentation in multi-modality microscopy images.

https://neurips22-cellseg.grand-challenge.org/

Please cite the following paper if this dataset is used in your research. 

 

@article{NeurIPS-CellSeg,
      title = {The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions},
      author = {Jun Ma and Ronald Xie and Shamini Ayyadhury and Cheng Ge and Anubha Gupta and Ritu Gupta and Song Gu and Yao Zhang and Gihun Lee and Joonkee Kim and Wei Lou and Haofeng Li and Eric Upschulte and Timo Dickscheid and José Guilherme de Almeida and Yixin Wang and Lin Han and Xin Yang and Marco Labagnara and Vojislav Gligorovski and Maxime Scheder and Sahand Jamal Rahi and Carly Kempster and Alice Pollitt and Leon Espinosa and Tâm Mignot and Jan Moritz Middeke and Jan-Niklas Eckardt and Wangkai Li and Zhaoyang Li and Xiaochen Cai and Bizhe Bai and Noah F. Greenwald and David Van Valen and Erin Weisbart and Beth A. Cimini and Trevor Cheung and Oscar Brück and Gary D. Bader and Bo Wang},
      journal = {Nature Methods},
      volume={21},
      pages={1103–1113},
      year = {2024},
      doi = {https://doi.org/10.1038/s41592-024-02233-6}
  }

 

This is an instance segmentation task where each cell has an individual label under the same category (cells). The training set contains both labeled images and unlabeled images. You can only use the labeled images to develop your model but we encourage participants to try to explore the unlabeled images through weakly supervised learning, semi-supervised learning, and self-supervised learning.

 

The images are provided with original formats, including tiff, tif, png, jpg, bmp... The original formats contain the most amount of information for competitors and you have free choice over different normalization methods. For the ground truth, we standardize them as tiff formats.

 

We aim to maintain this challenge as a sustainable benchmark platform. If you find the top algorithms (https://neurips22-cellseg.grand-challenge.org/awards/) don't perform well on your images, welcome to send us the dataset (neurips.cellseg@gmail.com)! We will include them in the new testing set and credit your contributions on the challenge website!

 

Dataset License: CC-BY-NC-ND

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