Abstract: Analyzing large amounts of microscopy images in a FAIR manner is an ongoing challenge, made harder by the large diversity of image file formats and processing approaches. Recent work on OME-Zarr, a community-driven next-generation file format, offers the chance to create more shareable bioimage analysis workflows. At the BioVisionCenter, we are developing open-source resources for OME-Zarr-based image analysis. First, we propose extensions to OME-Zarr to incorporate tabular data for region of interest definitions, feature measurements and image metadata. Second, we have built a specification for interoperable image processing tasks that handle image data in the OME-Zarr format. Third, we have developed the Fractal framework to handle scalable and accessible image analysis workflows using OME-Zarrs. The Fractal framework consists of a server backend & web-frontend that handle modular image processing workflows. It facilitates the design and execution of reproducible workflows to convert images into OME-Zarrs and apply advanced processing operations to them at scale, without the need for expertise in programming or large image file handling. The Fractal community has made over 100 tasks publicly available under permissive licenses that enable converting vendor data to OME-Zarr, performing segmentation using a variety of segmentation networks, extracting high-dimensional measurements from large datasets, processing multiplexed images and many more analysis needs. By integrating with existing OME-Zarr viewers like ViZarr, napari and MoBIE, this allows for interactive visualisation of images and their processing results. And thanks to the federated deployment approach of Fractal, it can be hosted on a given institution's server and integrated with local HPC to process terabytes of image data.