Technology
ZeroCostDL4Mic
GitHub:
- HenriquesLab/ZeroCostDL4Mic
Publication: Chamier et al. Nature communications 2021
- 479
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:
-
Provides access to powerful GPUs on Google Colab for free to train deep learning models, removing the need for expensive local servers or hardware.
-
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.
-
The notebooks have an easy-to-use interface allowing non-experts to train models by uploading their data and configuring parameters with minimal coding.
-
Allows comprehensive evaluation of model performance using quantitative quality control metrics. This allows optimization and ensures reliability before models are deployed.
-
Support for data augmentation and transfer learning to improve model performance and simplify the training process.
-
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
|
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, January 2024 Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ-eSRRF, NanoJ-SQUIRREL, NanoJ-SRRF and ZeroCostDL4Mic Funded by: CZI, EMBO, ERC, H2021 and H2022 DOI: 10.1242/jcs.261545 |
|
|
CellTracksColab is a platform that enables compilation, analysis, and exploration of cell tracking data Estibaliz Gómez-de-Mariscal, Hanna Grobe, Joanna W Pylvänäinen, Laura Xénard, Ricardo Henriques, Jean-Yves Tinevez, Guillaume Jacquemet Published in PLOS Biology, January 2024 Technologies: CARE, CellTracksColab and ZeroCostDL4Mic DOI: 10.1371/journal.pbio.3002740 |
|
|
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, January 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 |
|
|
PhotoFiTT - A Quantitative Framework for Assessing Phototoxicity in Live-Cell Microscopy Experiments Mario Del Rosario, Estibaliz Gómez-de-Mariscal, Leonor Morgado, Raquel Portela, Guillaume Jacquemet, Pedro M. Pereira, Ricardo Henriques Preprint published in bioRxiv, January 2024 Technologies: DL4MicEverywhere, PhotoFiTT and ZeroCostDL4Mic Funded by: CZI, EMBO, ERC, FCT, H2021 and H2022 DOI: 10.1101/2024.07.16.603046 |
|
|
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, January 2023 Technologies: BioImage Model Zoo, CLEM-Reg and ZeroCostDL4Mic Funded by: CZI and ERC DOI: 10.1101/2023.05.11.540445 |
|
|
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, DeepBacs, Fast4DReg, NanoJ, NanoJ-eSRRF, NanoJ-Fluidics 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 |
|
|
This Microtubule Does Not Exist - Super‐Resolution Microscopy Image Generation by a Diffusion Model Alon Saguy, Tav Nahimov, Maia Lehrman, Estibaliz Gómez-de-Mariscal, Iván Hidalgo-Cenalmor, Onit Alalouf, Ashwin Balakrishnan, Mike Heilemann, Ricardo Henriques, Yoav Shechtman Published in Small Methods, January 2023 Technologies: ZeroCostDL4Mic Funded by: CZI, EMBO, ERC, H2021 and H2022 DOI: 10.1002/smtd.202400672 |
|
|
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, January 2023 Technologies: CARE, DeepAutoFocus, DeepBacs and ZeroCostDL4Mic Funded by: EMBO, ERC, H2021 and H2022 DOI: 10.1101/2023.10.16.562172 |
|
|
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, January 2022 Technologies: NanoJ-VirusMapper, Nuclear-Pores as references and ZeroCostDL4Mic Funded by: EMBO, ERC and Wellcome Trust DOI: 10.1098/rsob.220079 |
|
|
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, CARE and ZeroCostDL4Mic Funded by: CZI, EMBO, ERC and H2021 DOI: 10.1101/2022.06.07.495102 |
|
|
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, January 2022 Technologies: BioImage Model Zoo, CARE, DeepBacs, NanoJ, NanoJ-SQUIRREL and ZeroCostDL4Mic Funded by: CZI, ERC, FCT and Wellcome Trust DOI: 10.1038/s42003-022-03634-z |
|
|
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, January 2021 Technologies: CARE, 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 |
|
|
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: BioImage Model Zoo and ZeroCostDL4Mic Funded by: EMBO, ERC and Wellcome Trust DOI: 10.1038/s41592-021-01284-3 |
|
|
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, January 2021 Technologies: NanoJ-VirusMapper, Super-Beacons and ZeroCostDL4Mic Funded by: Wellcome Trust DOI: 10.3390/v13020233 |
|
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 |
|
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: 10 |
|
|
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: 13 |
|
|
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: 14 |
|
Unveiling live-cell viral replication at the nanoscale Ricardo Henriques Funded by: EMBO - Installation Grant Duration: January 2021 - January 2026 Publications: 21 |