ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy

Lucas von Chamier; Romain F. Laine; Johanna Jukkala; Christoph K. Spahn; Daniel Krentzel; Elias Nehme; Martina Lerche; Sara Hernández‐Pérez; Pieta K. Mattila; Eleni Karinou; Seamus J. Holden; Ahmet Can Solak; Alexander Krull; Tim-Oliver Buchholz; Martin L. Jones; Löıc A. Royer; Christophe Leterrier; Yoav Shechtman; Florian Jug; Mike Heilemann; Guillaume Jacquemet; Ricardo Henriques

doi:10.1038/s41467-021-22518-0

BioinformaticsNature CommunicationsApr 15, 2021

Peer-reviewed journal article

ZeroCostDL4Mic: an open platform to use Deep-Learning in Microscopy

Lucas von Chamier iD, Romain F. Laine iD, Johanna Jukkala, Christoph K. Spahn iD, Daniel Krentzel iD, Elias Nehme iD, Martina Lerche iD, Sara Hernández‐Pérez iD, Pieta K. Mattila iD, Eleni Karinou iD, Seamus J. Holden iD, Ahmet Can Solak iD, Alexander Krull iD, Tim-Oliver Buchholz iD, Martin L. Jones iD, Löıc A. Royer iD, Christophe Leterrier iD, Yoav Shechtman iD, Florian Jug iD, Mike Heilemann iD, Guillaume Jacquemet iD, Ricardo Henriques iD

Abstract

The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation. We demonstrate the application of the platform to study multiple biological processes.

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