Fractal: Towards FAIR bioimage analysis at scale with OME-Zarr-native workflows

Authors

Lüthi, J., Cerrone, L., Comparin, T., Hess, M., Hornbachner, R., Tschan, A., Glasner de Medeiros, G. Q., Repina, N. A., Cantoni, L. K., Steffen, F. D., Bourquin, J.-P., Liberali, P., Pelkmans, L., Uhlmann, V.

Abstract

The rapid growth in microscopy data volume, dimensionality, and diversity urgently calls for scalable and reproducible analysis frameworks. While efforts on the open OME-Zarr format have helped standardize the storage of large microscopy datasets, solutions for standardized processing are still lacking. Here, we introduce two complementary contributions to address this gap: 1) the Fractal task specification, defining OME-Zarr processing units that can interoperate across computational environments and workflow engines, and 2) the Fractal platform, using this specification to enable scalable and modular OME-Zarr-native analysis workflows. We demonstrate their use across diverse biological research data, including terabyte-scale multiplexed, volumetric, and time-lapse imaging. In a clinical setting, we show that Fractal workflows achieve near-identical quantification of millions of cells across independent deployments, demonstrating the reproducibility required for translational applications. With its growing community of contributors, the Fractal ecosystem provides a foundation for FAIR microscopy image analysis relying on open file formats.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 10 Mar 2026.

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