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Rescale4DL


GitHub:
- HenriquesLab/Rescale4DL

Publication: Ferreira et al. bioRxiv 2025
- 2

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

mAIcrobe - an open-source framework for high-throughput bacterial image analysis
António D. Brito, Dominik Alwardt, Beatriz de P. Mariz, Sérgio R. Filipe, Mariana G Pinho, Bruno M. Saraiva, Ricardo Henriques
Preprint published in bioRxiv, October 2025
Technologies: DeepBacs (), mAIcrobe (), Rescale4DL () and ZeroCostDL4Mic ()
Funded by: EMBO, ERC, H2021 and H2022
DOI: 10.1101/2025.10.21.683709
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
Spatiotemporal Coordination of Guidance Cues Directs Multipolar Migration During Retinal Lamination
Jaakko I Lehtimäki, Jingtao Lilue, Mario Del Rosario, Elisa Nerli, Ricardo Henriques, Caren Norden
Preprint published in bioRxiv, July 2025
Technologies: BioImage Model Zoo (), CARE (), DL4MicEverywhere (), Rescale4DL () and ZeroCostDL4Mic ()
Funded by: CZI, ERC, H2021 and H2022
DOI: 10.1101/2025.07.23.666134
ReScale4DL - Balancing Pixel and Contextual Information for Enhanced Bioimage Segmentation
Mariana G. Ferreira, Bruno M. Saraiva, António D. Brito, Mariana G. Pinho, Ricardo Henriques, Estibaliz Gómez-de-Mariscal
Preprint published in bioRxiv, April 2025
Technologies: BioImage Model Zoo (), DeepBacs (), DL4MicEverywhere (), NanoPyx (), Rescale4DL () and ZeroCostDL4Mic ()
Funded by: CZI, EMBO, ERC, H2021 and H2022
DOI: 10.1101/2025.04.09.647871