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ZeroCostDL4Mic


GitHub:
- HenriquesLab/ZeroCostDL4Mic

Publication: Chamier et al. Nature Communications 2021
- 433

ZeroCostDL4Mic is an open-source platform that aims to make deep learning more accessible for microscopy image analysis. It allows researchers with limited coding or computing expertise to easily train, evaluate, and apply deep learning models for various bioimage analysis tasks.

The key idea behind ZeroCostDL4Mic is to leverage Google Colab, a free cloud-based service, to provide the necessary computing resources for deep learning without requiring users to purchase expensive hardware. It is implemented as a collection of user-friendly Jupyter notebooks with graphical user interfaces that guide users through the workflow of training and using deep learning models.

Some key features of ZeroCostDL4Mic:

  1. Provides access to powerful GPUs on Google Colab for free to train deep learning models, removing the need for expensive local servers or hardware.

  2. Implements several state-of-the-art deep learning models for common microscopy image analysis tasks like segmentation, object detection, image restoration, super-resolution microscopy, and image-to-image translation.

  3. The notebooks have an easy-to-use interface allowing non-experts to train models by uploading their data and configuring parameters with minimal coding.

  4. Allows comprehensive evaluation of model performance using quantitative quality control metrics. This allows optimization and ensures reliability before models are deployed.

  5. Support for data augmentation and transfer learning to improve model performance and simplify the training process.

  6. Trained models can be exported and used with other platforms like ImageJ or Fiji. The notebooks can also be combined into analysis pipelines.

ZeroCostDL4Mic makes deep learning more practical and accessible to microscopy researchers by providing a user-friendly platform to train and apply advanced models using free cloud computing resources. This allows those with limited technical expertise to leverage state-of-the-art techniques for analyzing their imaging data. The open-source and modular nature also allows advanced users to build on top of it. Overall, it has the potential to greatly accelerate the adoption of deep learning in bioimage analysis.


Publications featuring ZeroCostDL4Mic

DL4MicEverywhere - deep learning for microscopy made flexible, shareable and reproducible
Iván Hidalgo-Cenalmor, Joanna W. Pylvänäinen, Mariana G. Ferreira, Craig T. Russell, Alon Saguy, Ignacio Arganda-Carreras, Yoav Shechtman, Guillaume Jacquemet, Ricardo Henriques, Estibaliz Gómez-de-Mariscal
Paper published in Nature Methods, May 2024
Technologies: BioImage Model Zoo, DL4MicEverywhere and ZeroCostDL4Mic
Funded by: EMBO, ERC, H2021 and H2022
News: Labonline, MSN, AZoRobotics and Aamuset Kaupunkimedia
Blogs: news-medical.net
DOI: 10.1038/s41592-024-02295-6
Harnessing artificial intelligence to reduce phototoxicity in live imaging
Estibaliz Gómez-de-Mariscal, Mario Del Rosario, Joanna W. Pylvänäinen, Guillaume Jacquemet, Ricardo Henriques
Perspective published in Journal of Cell Science, February 2024
Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ-SRRF and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC, H2021 and H2022
DOI: 10.1242/jcs.261545
Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth
Christoph Spahn, Stuart Middlemiss, Estibaliz Gómez-de-Mariscal, Ricardo Henriques, Helge B. Bode, Séamus Holden, Mike Heilemann
Preprint published in bioRxiv, October 2023
Technologies: CARE, DeepBacs and ZeroCostDL4Mic
Funded by: EMBO, ERC, H2021 and H2022
DOI: 10.1101/2023.10.16.562172
CLEM-Reg - An automated point cloud based registration algorithm for correlative light and volume electron microscopy
Daniel Krentzel, Matouš Elphick, Marie-Charlotte Domart, Christopher J. Peddie, Romain F. Laine, Ricardo Henriques, Lucy M. Collinson, Martin L. Jones
Preprint published in bioRxiv, May 2023
Technologies: CARE and ZeroCostDL4Mic
Funded by: CZI and ERC
DOI: 10.1101/2023.05.11.540445
CellTracksColab—A platform for compiling, analyzing, and exploring tracking data
Estibaliz Gómez-de-Mariscal, Hanna Grobe, Joanna W Pylvänäinen, Laura Xénard, Ricardo Henriques, Jean-Yves Tinevez, Guillaume Jacquemet
Preprint published in bioRxiv, January 2023
Technologies: ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC, H2021 and H2022
DOI: 10.1101/2023.10.20.563252
Live-cell imaging in the deep learning era
Joanna W Pylvänäinen, Estibaliz Gómez-de-Mariscal, Ricardo Henriques, Guillaume Jacquemet
Review published in Current Opinion in Cell Biology, January 2023
Technologies: BioImage Model Zoo, CARE, DeepBacs, Fast4DReg, NanoJ, NanoJ-eSRRF, NanoJ-Fluidics, NanoJ-SRRF and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC and H2021
DOI: 10.1016/j.ceb.2023.102271
Fast4DReg–fast registration of 4D microscopy datasets
Joanna W Pylvänäinen, Romain F Laine, Bruno MS Saraiva, Sujan Ghimire, Gautier Follain, Ricardo Henriques, Guillaume Jacquemet
Paper published in Journal of Cell Science, January 2023
Technologies: CARE, Fast4DReg, NanoJ and ZeroCostDL4Mic
Funded by: CZI and ERC
DOI: 10.1242/jcs.260728
Roadmap on deep learning for microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, Michael Hecht, Artur Yakimovich, Kristina Monakhova, Laura Waller, Ivo F Sbalzarini, Christopher A Metzler, Mingyang Xie, Kevin Zhang, Isaac CD Lenton, Halina Rubinsztein-Dunlop, Daniel Brunner, Bijie Bai, Aydogan Ozcan, Daniel Midtvedt, Hao Wang, Nataša Sladoje, Joakim Lindblad, Jason T Smith, Marien Ochoa, Margarida Barroso, Xavier Intes, Tong Qiu, Li-Yu Yu, Sixian You, Yongtao Liu, Maxim A Ziatdinov, Sergei V Kalinin, Arlo Sheridan, Uri Manor, Elias Nehme, Ofri Goldenberg, Yoav Shechtman, Henrik K Moberg, Christoph Langhammer, Barbora Špačková, Saga Helgadottir, Benjamin Midtvedt, Aykut Argun, Tobias Thalheim, Frank Cichos, Stefano Bo, Lars Hubatsch, Jesus Pineda, Carlo Manzo, Harshith Bachimanchi, Erik Selander, Antoni Homs-Corbera, Martin Fränzl, Kevin de Haan, Yair Rivenson, Zofia Korczak, Caroline Beck Adiels, Mite Mijalkov, Dániel Veréb, Yu-Wei Chang, Joana B Pereira, Damian Matuszewski, Gustaf Kylberg, Ida-Maria Sintorn, Juan C Caicedo, Beth A Cimini, Muyinatu A Lediju Bell, Bruno M Saraiva, Guillaume Jacquemet, Ricardo Henriques, Wei Ouyang, Trang Le, Estibaliz Gómez-de-Mariscal, Daniel Sage, Arrate Muñoz-Barrutia, Ebba Josefson Lindqvist, Johanna Bergman
Preprint published in arXiv, January 2023
Technologies: BioImage Model Zoo, CARE and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC and H2021
DOI: 10.48550/arXiv.2303.03793
Mapping molecular complexes with super-resolution microscopy and single-particle analysis
Afonso Mendes, Hannah S. Heil, Simao Coelho, Christophe Leterrier, Ricardo Henriques
Review published in Open Biology, July 2022
Technologies: NanoJ-VirusMapper, Nuclear-Pores as references and ZeroCostDL4Mic
Funded by: EMBO, ERC and Wellcome Trust
DOI: 10.1098/rsob.220079
DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches
Christoph Spahn, Estibaliz Gómez-de-Mariscal, Romain F. Laine, Pedro M. Pereira, Lucas von Chamier, Mia Conduit, Mariana G. Pinho, Guillaume Jacquemet, Séamus Holden, Mike Heilemann, Ricardo Henriques
Paper published in Communications Biology, July 2022
Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ, NanoJ-SQUIRREL, NanoJ-SRRF and ZeroCostDL4Mic
Funded by: CZI, ERC, FCT and Wellcome Trust
DOI: 10.1038/s42003-022-03634-z
Bioimage model zoo - a community-driven resource for accessible deep learning in bioimage analysis
Wei Ouyang, Fynn Beuttenmueller, Estibaliz Gómez-de-Mariscal, Constantin Pape, Tom Burke, Carlos Garcia-López-de-Haro, Craig Russell, Lucía Moya-Sans, Cristina de-la-Torre-Gutiérrez, Deborah Schmidt, Dominik Kutra, Maksim Novikov, Martin Weigert, Uwe Schmidt, Peter Bankhead, Guillaume Jacquemet, Daniel Sage, Ricardo Henriques, Arrate Muñoz-Barrutia, Emma Lundberg, Florian Jug, Anna Kreshuk
Preprint published in BioRxiv, January 2022
Technologies: BioImage Model Zoo and ZeroCostDL4Mic
Funded by: CZI, EMBO, ERC and H2021
DOI: 10.1101/2022.06.07.495102
Democratising deep learning for microscopy with ZeroCostDL4Mic
Lucas von Chamier, Romain F. Laine, Johanna Jukkala, Christoph Spahn, Daniel Krentzel, Elias Nehme, Martina Lerche, Sara Hernández-Pérez, Pieta K. Mattila, Eleni Karinou, Séamus Holden, Ahmet Can Solak, Alexander Krull, Tim-Oliver Buchholz, Martin L. Jones, Loïc A. Royer, Christophe Leterrier, Yoav Shechtman, Florian Jug, Mike Heilemann, Guillaume Jacquemet, Ricardo Henriques
Paper published in Nature Communications, April 2021
Technologies: CARE, FBSR-TFM, NanoJ, NanoJ-SQUIRREL, NanoJ-SRRF and ZeroCostDL4Mic
Funded by: EMBO, ERC and Wellcome Trust
News: AZO Life Sciences, Drug Target Review, Nanotechnology Now and The Medical News
Blogs: Microbiome Digest - Bik's Picks
DOI: 10.1038/s41467-021-22518-0
Application of Super-Resolution and Advanced Quantitative Microscopy to the Spatio-Temporal Analysis of Influenza Virus Replication
Emma Touizer, Christian Sieben, Ricardo Henriques, Mark Marsh, Romain F. Laine
Review published in Viruses, February 2021
Technologies: NanoJ-VirusMapper, Super-Beacons and ZeroCostDL4Mic
Funded by: Wellcome Trust
DOI: 10.3390/v13020233
Avoiding a replication crisis in deep-learning-based bioimage analysis
Romain F Laine, Ignacio Arganda-Carreras, Ricardo Henriques, Guillaume Jacquemet
Perspective published in Nature methods, January 2021
Technologies: CARE and ZeroCostDL4Mic
Funded by: EMBO, ERC and Wellcome Trust
DOI: 10.1038/s41592-021-01284-3
The cell biologist's guide to super-resolution microscopy
Guillaume Jacquemet, Alexandre F. Carisey, Hellyeh Hamidi, Ricardo Henriques, Christophe Leterrier
Review published in Journal of Cell Science, June 2020
Technologies: CARE, NanoJ, NanoJ-Fluidics, NanoJ-SRRF, Nuclear-Pores as references and ZeroCostDL4Mic
Funded by: BBSRC and Wellcome Trust
News: ScienMag and EurekAlert!
DOI: 10.1242/jcs.240713

Funding contributing to ZeroCostDL4Mic

SMALS - Smart Microscopy for Adaptative Live Super-resolution imaging to elucidate the initial steps of the HIV viral transmission
Estibaliz Gómez-de-Mariscal
Alias: SMALS
Funded by: FCT - CEEC Individual
Duration: April 2024 - March 2027
Quinoline-Derived Foldamers - A novel approach to combat antibiotic multiresistant and persistent
Pedro M. Mateus, Pedro Matos Pereira, Ricardo Henriques
Funded by: FCT - LS4Future Seed Funding
Duration: March 2024 - September 2024
3D Nanoscope - a highly accessible, high-performance device for live cell nanoscopy
Arturo G. Vesga
Alias: 3DNanoScope4All
Funded by: Marie Curie - HORIZON TMA MSCA Postdoctoral Fellowships
Duration: March 2024 - February 2026
Sub-cellular Metabolic Compartmentalization During Oocyte Development
Zita Carvalho dos Santos, Ricardo Henriques, Jorge Carvalho
Funded by: CZI - Measuring Metabolism Across Scales
Duration: January 2024 - December 2026
A transformative data-driven live-cell super-resolution microscopy development to elucidate the initial steps of effective viral transmission
Estibaliz Gómez-de-Mariscal
Funded by: EMBO - Postdoctoral Fellowships
Duration: July 2022 - June 2024
Publications: 5
Real-Time high-content Super-Resolution Imaging of ES Cell States
Eran Meshorer, Ricardo Henriques, Anna Kreshuk, Sandrine Lévêque-Fort, Nicolas Bourg, Genevieve Almouzni
Alias: RT-SuperES
Funded by: H2022 - EIC Pathfinder Open
Duration: April 2022 - March 2027
Publications: 8
VP-CLEM-KIT - a pipeline for democratising volumetric visual proteomics
Lucy Collinson, Ricardo Henriques, Paul French
Funded by: CZI - Visual Proteomics Imaging
Duration: December 2021 - June 2024
Publications: 12
Artificial Intelligence for Image Data Analysis in the Life Sciences
Anna Kreshuk, Florian Jug, Ricardo Henriques, Wei Ouyang, Arrate Muñoz-Barrutia, Emma Lundberg, Matthew Hartley
Alias: AI4Life
Funded by: H2021 - INFRA
Duration: September 2021 - August 2025
Publications: 12
Unveiling live-cell viral replication at the nanoscale
Ricardo Henriques
Funded by: EMBO - Installation Grant
Duration: January 2021 - January 2026
Publications: 19