Technology

Rescale4DL

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GitHub:
- HenriquesLab/Rescale4DL

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Publication: Overview of the 'Rescale4DL' technology, its features, associated publications, funding and more.
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ReScale4D is a systematic approach for determining optimal image resolution in deep learning-based microscopy segmentation, balancing accuracy with acquisition/storage costs. Following this approach, researchers can improve the sustainability and cost-effectiveness of bioimaging studies by reducing data and computing needs while optimising microscopy techniques.

Publications featuring Rescale4DL

CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy Daniel Krentzel, Matouš Elphick, Marie-Charlotte Domart, Christopher J Peddie, Romain F Laine, Cameron Shand, Ricardo Henriques, Lucy M Collinson, Martin L Jones Paper published in Nature Methods, September 2025 Technologies: BioImage Model Zoo (), CARE (), DL4MicEverywhere (), Rescale4DL () and ZeroCostDL4Mic () Funded by: CZI, ERC, H2021 and H2022 DOI: 10.1038/s41592-025-02794-0