NeurIPS 2022 Cell Segmentation Competition Dataset

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 ([e-mail])! We will include them in the new testing set and credit your contributions on the challenge website!

Dataset License: CC-BY-NC-ND

本数据集为NeurIPS 2022竞赛官方数据集：多模态显微图像细胞分割任务。

官方竞赛网站：https://neurips22-cellseg.grand-challenge.org/

若您的研究中使用本数据集，请引用如下论文：

@article{NeurIPS-CellSeg,
title = {《多模态细胞分割挑战赛：迈向通用解决方案》},
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 = {《自然·方法学》},
volume={21},
pages={1103–1113},
year = {2024},
doi = {https://doi.org/10.1038/s41592-024-02233-6}
}

本任务属于实例分割任务，同一类别（细胞）下的每个细胞均配有独立标注标签。训练集同时包含带标注图像与无标注图像：您仅可使用带标注图像开发模型，但我们鼓励参赛选手通过弱监督学习、半监督学习与自监督学习探索无标注图像的应用潜力。

图像以原始格式提供，涵盖tiff、tif、png、jpg、bmp等格式。原始格式可保留最完整的信息供参赛者使用，您可自由选择各类归一化方法。对于标注真值（ground truth），我们统一采用tiff格式进行标准化存储。

我们致力于将本竞赛打造为可持续的基准评测平台。若您发现顶级参赛算法（链接：https://neurips22-cellseg.grand-challenge.org/awards/）在您的图像上表现不佳，欢迎向我们发送您的数据集（邮箱：[e-mail]）！我们将把您提供的数据集纳入新的测试集，并在竞赛官网中对您的贡献予以致谢与署名。

数据集许可协议：CC-BY-NC-ND