2023
DOI: 10.1007/s00418-023-02209-1
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OME-Zarr: a cloud-optimized bioimaging file format with international community support

Josh Moore,
Daniela Basurto-Lozada,
Sébastien Besson
et al.

Abstract: A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the cloud-optimized format itself—OME-Zarr—along with tools and data resources availab… Show more

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Cited by 44 publications
(25 citation statements)
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“…However, we are encouraging data providers to submit datasets that can be easily converted to OME-Zarr or provide data in a format that is compatible with our on-demand transformer. More information on the on-demand transformer and our plan to serve OME-Zarr dynamically at BIL can be found in the publication describing community adoption of OME-Zarr 12 .…”
Section: Discussionmentioning
confidence: 99%
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“…However, we are encouraging data providers to submit datasets that can be easily converted to OME-Zarr or provide data in a format that is compatible with our on-demand transformer. More information on the on-demand transformer and our plan to serve OME-Zarr dynamically at BIL can be found in the publication describing community adoption of OME-Zarr 12 .…”
Section: Discussionmentioning
confidence: 99%
“…Most deposited images are whole brain volumetric stacks in native TIFF and JPEG 2000 image file formats. Currently, we are encouraging the use of high-performance next-generation file formats (NGFF) 11 such as multiscale OME-Zarr 12 , which include multi-resolution image pyramids optimized for rapid visualization and scalable analysis. While BIL continues to accommodate historically accepted file formats, datasets in these formats may not leverage the complete range of visualization capabilities now provided by BIL as discussed below.…”
Section: Methodsmentioning
confidence: 99%
“…Building on the innovative approach for 3D spot detection, U-FISH also employs a strategic method for handling large-scale multidimensional-image inference, particularly crucial for large images stored in formats such as OME-Zarr [23]. This strategy involves reading and processing individual data blocks from the disk, one at a time, and then storing the processed results back onto the disk(Fig.…”
Section: Extends U-fish To 3d Fish Signal Detectionmentioning
confidence: 99%
“…This package includes network architectures for UFish-Net (Our network), DetNet, and SpotLearn, all implemented using PyTorch, and allows for the integration of custom network architectures by users. Importantly, U-FISH supports a wide range of file formats, including TIFF, OME-Zarr [23], and N5, making it versatile for input data types. It is also capable of handling multi-dimensional images, from 2D (x, y) to 4D (tczxy), ensuring comprehensive applicability across various imaging scenarios.…”
Section: B)mentioning
confidence: 99%
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