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

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.