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CaImAn: An open source tool for scalable Calcium Imaging data Analysis

Andrea Giovannucci
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Pat Gunn
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Jérémie Kalfon
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Ann Koay
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Jiannis Taxidis
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Farzaneh Naja
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Jeffey L Gauthier
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David W Tank
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Eftychios A Pnevmatikakis
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Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CAIMAN, an open-source library for calcium imaging data analysis. CAIMAN provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CAIMAN is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CAIMAN we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CAIMAN achieves near-human performance in detecting locations of active neurons.
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Dates and versions

hal-01812108 , version 1 (11-06-2018)



Andrea Giovannucci, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Ann Koay, et al.. CaImAn: An open source tool for scalable Calcium Imaging data Analysis. 2018. ⟨hal-01812108⟩
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