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

Luca Sità (), Marco Brondi (), Pedro Lagomarsino de Leon Roig, Sebastiano Curreli, Mariangela Panniello, Dania Vecchia and Tommaso Fellin ()
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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
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

Nature Communications, 2022, vol. 13, issue 1, 1-22

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-29180-0

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