---
pretty_name: Revvity-25
license: cc-by-nc-4.0
modalities:
- image
task_categories:
- image-segmentation
- object-detection
task_ids:
- instance-segmentation
annotations_creators:
- expert-generated
size_categories:
- n<1K
language:
- en
tags:
- image
- json
- computer-vision
- transformers
- instance-segmentation
- object-detection
- coco
- unet
- microscopy
- cells
- biomedical
- cell-segmentation
- biomedical-imaging
- microscopy-images
- brightfield
- cancer-cells
- semantic-segmentation
- datasets
- fiftyone
- cell
- amodal
---


<h1 align="center">Revvity-25 (CVPRW 2025)</h1>

<div align="center">
  
<!-- [![Conference](https://img.shields.io/badge/CVPRW-2025-blue)](https://openaccess.thecvf.com/CVPR2025_workshops/CVMI) -->
[![Paper](https://img.shields.io/badge/Paper-CVPRW%202025-brightgreen)](https://www.arxiv.org/abs/2508.01928)&nbsp;&nbsp;
[![GitHub](https://img.shields.io/badge/GitHub-IAUNet-black?logo=github)](https://github.com/SlavkoPrytula/IAUNet)&nbsp;
[![GitHub Stars](https://img.shields.io/github/stars/SlavkoPrytula/IAUNet?style=social)](https://github.com/SlavkoPrytula/IAUNet)&nbsp;&nbsp;
[![Project WebPage](https://img.shields.io/badge/Project-webpage-%23fc4d5d)](https://slavkoprytula.github.io/IAUNet/)

</div>


<div align="center" style="margin-top: 10px; margin-bottom: 20px;">
    <span class="author-block">
        <a href="https://scholar.google.com/citations?user=RLf-ytQAAAAJ&hl">Yaroslav Prytula</a><sup>1,2</sup>
    </span> &nbsp;|&nbsp;
    <span class="author-block">
        <a href="https://scholar.google.com/citations?user=tnvq360AAAAJ&hl=en">Illia Tsiporenko</a><sup>1</sup>
    </span> &nbsp;|&nbsp;
    <span class="author-block">
        <a href="https://scholar.google.com/citations?user=Y_aAGL8AAAAJ&hl=en">Ali Zeynalli</a><sup>1</sup>
    </span> &nbsp;|&nbsp;
    <span class="author-block">
        <a href="https://scholar.google.com/citations?user=IOuDrrEAAAAJ&hl=en">Dmytro Fishman</a><sup>1,3</sup>
    </span>
    <div class="is-size-5 publication-authors">
      <span class="author-block"><sup>1</sup>Institute of Computer Science, University of Tartu,</span><br>
      <span class="author-block"><sup>2</sup>Ukrainian Catholic University,</span>
      <span class="author-block"><sup>3</sup>STACC OÜ, Tartu, Estonia</span>
    </div>
</div>


<div align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/662798e43e2a8eec1b53ed1a/R-NJgg-VBjO2GSqainotU.png" alt="Revvity-25 preview" width="850">
</div>


🔥 Paper: [https://arxiv.org/abs/2508.01928](https://arxiv.org/abs/2508.01928) \
⭐️ Github: [https://github.com/SlavkoPrytula/IAUNet](https://github.com/SlavkoPrytula/IAUNet) \
🌐 Project page: [https://slavkoprytula.github.io/IAUNet/](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](https://www.arxiv.org/abs/2508.01928)** 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](https://bcv.cs.ut.ee/datasets/)

## Directory structure

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

---

## Citing Revvity-25

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

```bibtex
@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}
}
````

---

## License

[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)

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](LICENSE) file or visit [Creative Commons](https://creativecommons.org/licenses/by-nc/4.0/).

---

## Contact

📧 [s.prytula@ucu.edu.ua](mailto:s.prytula@ucu.edu.ua) or [yaroslav.prytula@ut.ee](mailto:yaroslav.prytula@ut.ee)

---

## Acknowledgements

This work was supported by [Revvity](https://www.revvity.com/) 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](https://bcv.cs.ut.ee/) 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.