Synthesizing Annotated Cell Microscopy Images with Generative Adversarial Networks

Duway Nicolas Lesmes-Leon, Duway Nicolas Lesmes-Leon, Miro Miranda, Miro Miranda, Maria Caroprese, Gillian Lovell, Andreas Dengel, Andreas Dengel, Sheraz Ahmed

2025

Abstract

Data scarcity and annotation limit the quantitation of cell microscopy images. Data acquisition, preparation, and annotation are costly and time-consuming. Additionally, cell annotation is an error-prone task that requires personnel with specialized knowledge. Generative artificial intelligence is an alternative to alleviate these limitations by generating realistic images from an unknown data probabilistic distribution. Still, extra effort is needed since data annotation remains an independent task of the generative process. In this work, we assess whether generative models learn meaningful instance segmentation-related features, and their potential to produce realistic annotated images. We present a single-channel grayscale segmentation mask pipeline that differentiates overlapping objects while minimizing the number of labels. Additionally, we propose a modified version of the established StyleGAN2 generator that synthesizes images and segmentation masks simultaneously without additional components. We tested our generative pipeline with LIVECell and TissueNet, two benchmark cell segmentation datasets. Furthermore, we augmented a segmentation deep learning network with synthetic samples and illustrated improved or on-par performance compared to its non-augmented version. Our results support that the features learned by generative models are relevant in the annotation context. With adequate data preparation and regularization, generative models are capable of producing realistic annotated samples cost-effectively.

Paper Citation

in Harvard Style

Lesmes-Leon D., Miranda M., Caroprese M., Lovell G., Dengel A. and Ahmed S. (2025). Synthesizing Annotated Cell Microscopy Images with Generative Adversarial Networks. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 592-599. DOI: 10.5220/0013163200003890

in Bibtex Style

@conference{icaart25,
author={Duway Lesmes-Leon and Miro Miranda and Maria Caroprese and Gillian Lovell and Andreas Dengel and Sheraz Ahmed},
title={Synthesizing Annotated Cell Microscopy Images with Generative Adversarial Networks},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2025},
pages={592-599},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013163200003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Synthesizing Annotated Cell Microscopy Images with Generative Adversarial Networks
SN - 978-989-758-737-5
AU - Lesmes-Leon D.
AU - Miranda M.
AU - Caroprese M.
AU - Lovell G.
AU - Dengel A.
AU - Ahmed S.
PY - 2025
SP - 592
EP - 599
DO - 10.5220/0013163200003890
PB - SciTePress