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

  1. Lucas von Chamier
  2. Romain F. Laine
  3. Johanna Jukkala
  4. Christoph Spahn
  5. Daniel Krentzel
  6. Elias Nehme
  7. Martina Lerche
  8. Sara Hernández-Pérez
  9. Pieta K. Mattila
  10. Eleni Karinou
  11. Séamus Holden
  12. Ahmet Can Solak
  13. Alexander Krull
  14. Tim-Oliver Buchholz
  15. Martin L. Jones
  16. Loïc A Royer
  17. Christophe Leterrier
  18. Yoav Shechtman
  19. Florian Jug
  20. Mike Heilemann
  21. Guillaume Jacquemet
  22. Ricardo Henriques
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Abstract

The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</ext-link>, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab.<ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</ext-link>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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