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Article dans une revue

Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities

Abstract : Studies of bacterial communities, biofilms and microbiomes, are multiplying due to their impact on health and ecology. Live imaging of microbial communities requires new tools for the robust identification of bacterial cells in dense and often inter-species populations, sometimes over very large scales. Here, we developed MiSiC, a general deep-learning-based 2D segmentation method that automatically segments single bacteria in complex images of interacting bacterial communities with very little parameter adjustment, independent of the microscopy settings and imaging modality. Using a bacterial predator-prey interaction model, we demonstrate that MiSiC enables the analysis of interspecies interactions, resolving processes at subcellular scales and discriminating between species in millimeter size datasets. The simple implementation of MiSiC and the relatively low need in computing power make its use broadly accessible to fields interested in bacterial interactions and cell biology.
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https://hal.umontpellier.fr/hal-03432709
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Soumis le : mercredi 17 novembre 2021 - 13:34:38
Dernière modification le : samedi 25 juin 2022 - 09:17:59
Archivage à long terme le : : vendredi 18 février 2022 - 19:52:34

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Swapnesh Panigrahi, Dorothée Murat, Antoine Le Gall, Eugénie Martineau, Kelly Goldlust, et al.. Misic, a general deep learning-based method for the high-throughput cell segmentation of complex bacterial communities. eLife, eLife Sciences Publication, 2021, 10, ⟨10.7554/eLife.65151⟩. ⟨hal-03432709⟩

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