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A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging

Author

Listed:
  • Luca Sità

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia)

  • Marco Brondi

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia)

  • Pedro Lagomarsino de Leon Roig

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia
    University of Genova)

  • Sebastiano Curreli

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia)

  • Mariangela Panniello

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia)

  • Dania Vecchia

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia)

  • Tommaso Fellin

    (Optical Approaches to Brain Function Laboratory, Istituto Italiano di Tecnologia)

Abstract

In vivo two-photon calcium imaging is a powerful approach in neuroscience. However, processing two-photon calcium imaging data is computationally intensive and time-consuming, making online frame-by-frame analysis challenging. This is especially true for large field-of-view (FOV) imaging. Here, we present CITE-On (Cell Identification and Trace Extraction Online), a convolutional neural network-based algorithm for fast automatic cell identification, segmentation, identity tracking, and trace extraction in two-photon calcium imaging data. CITE-On processes thousands of cells online, including during mesoscopic two-photon imaging, and extracts functional measurements from most neurons in the FOV. Applied to publicly available datasets, the offline version of CITE-On achieves performance similar to that of state-of-the-art methods for offline analysis. Moreover, CITE-On generalizes across calcium indicators, brain regions, and acquisition parameters in anesthetized and awake head-fixed mice. CITE-On represents a powerful tool to speed up image analysis and facilitate closed-loop approaches, for example in combined all-optical imaging and manipulation experiments.

Suggested Citation

  • Luca Sità & Marco Brondi & Pedro Lagomarsino de Leon Roig & Sebastiano Curreli & Mariangela Panniello & Dania Vecchia & Tommaso Fellin, 2022. "A deep-learning approach for online cell identification and trace extraction in functional two-photon calcium imaging," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-29180-0
    DOI: 10.1038/s41467-022-29180-0
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    2. Luke E Rogerson & Zhijian Zhao & Katrin Franke & Thomas Euler & Philipp Berens, 2019. "Bayesian hypothesis testing and experimental design for two-photon imaging data," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-27, August.
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