Revvity-25 (CVPRW 2025)

       

Yaroslav Prytula^1,2  |  Illia Tsiporenko¹  |  Ali Zeynalli¹  |  Dmytro Fishman^1,3

¹Institute of Computer Science, University of Tartu,
²Ukrainian Catholic University, ³STACC OÜ, Tartu, Estonia

🔥 Paper: https://arxiv.org/abs/2508.01928
⭐️ Github: https://github.com/SlavkoPrytula/IAUNet
🌐 Project page: https://slavkoprytula.github.io/IAUNet/

We present the Revvity-25 Full Cell Segmentation Dataset, a novel 2025 benchmark designed to advance cell segmentation research. One of our key contributions in the paper IAUNet: Instance-Aware U-Net is a novel cell instance segmentation dataset named Revvity-25. It includes 110 high-resolution 1080 x 1080 brightfield images, each containing, on average, 27 manually labeled and expert-validated cancer cells, totaling 2937 annotated cells. To our knowledge, this is the first dataset with accurate and detailed annotations for cell borders and overlaps, with each cell annotated using an average of 60 polygon points, reaching up to 400 points for more complex structures. Revvity-25 dataset provides a unique resource that opens new possibilities for testing and benchmarking models for modal and amodal semantic and instance segmentation.

• You can also check out and download the dataset from our webpage: Revvity-25

Directory structure

Revvity-25/
├── images/
└── annotations/
    ├── train.json
    └── valid.json

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Citing Revvity-25

If you use this work in your research, please cite:

@InProceedings{Prytula_2025_CVPR,
    author    = {Prytula, Yaroslav and Tsiporenko, Illia and Zeynalli, Ali and Fishman, Dmytro},
    title     = {IAUNet: Instance-Aware U-Net},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops},
    month     = {June},
    year      = {2025},
    pages     = {4739--4748}
}

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License

This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). You are free to share and adapt the work for non-commercial purposes as long as you give appropriate credit. For more details, see the LICENSE file or visit Creative Commons.

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Contact

📧 s.prytula@ucu.edu.ua or yaroslav.prytula@ut.ee

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Acknowledgements

This work was supported by Revvity and funded by the TEM-TA101 grant “Artificial Intelligence for Smart Automation.” Computational resources were provided by the High-Performance Computing Cluster at the University of Tartu 🇪🇪. We thank the Biomedical Computer Vision Lab for their invaluable support. We express gratitude to the Armed Forces of Ukraine 🇺🇦 and the bravery of the Ukrainian people for enabling a secure working environment, without which this work would not have been possible.