DeepCell Kiosk: scaling deep learning-enabled cellular image analysis with Kubernetes.

Journal: Nature methods
Published Date: Jan 4, 2021

Abstract

Deep learning is transforming the analysis of biological images, but applying these models to large datasets remains challenging. Here we describe the DeepCell Kiosk, cloud-native software that dynamically scales deep learning workflows to accommodate large imaging datasets. To demonstrate the scalability and affordability of this software, we identified cell nuclei in 10 1-megapixel images in ~5.5 h for ~US$250, with a cost below US$100 achievable depending on cluster configuration. The DeepCell Kiosk can be downloaded at https://github.com/vanvalenlab/kiosk-console ; a persistent deployment is available at https://deepcell.org/ .

Authors

  • Dylan Bannon
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Erick Moen
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Morgan Schwartz
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Enrico Borba
    Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
  • Takamasa Kudo
    Department of Chemical and Systems Biology, Stanford University, Stanford, California, United States of America.
  • Noah Greenwald
    Department of Cancer Biology, Stanford University, Stanford, CA, USA.
  • Vibha Vijayakumar
    Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA.
  • Brian Chang
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • Edward Pao
    2Department of Bioengineering, University of California, 420 Westwood Plaza, 5121 Engineering V, PO Box 951600, Los Angeles, CA 90095 USA.
  • Erik Osterman
    Cloud Posse, LLC, Pasadena, CA, USA.
  • William Graf
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA.
  • David Van Valen
    Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA, USA. vanvalen@caltech.edu.

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