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. 2021 Nov 12;12:6569. doi: 10.1038/s41467-021-26550-y

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Raw imaging data that can originate from a variety of sources; examples shown from 1-photon calcium imaging of Ciona intestinalis, 2-photon imaging of the mouse visual cortex neurons and volumetric 2-photon imaging of zebrafish. b Mesmerize’s highly modular design allows ROI extraction to be performed through a variety of methods such as CaImAn CNMF(E), NuSeT deep learning, or manually. ROIs can also be imported from Suite2p, ImageJ, or a custom module can be written using the API to import ROIs from other sources. c The Mesmerize Viewer lets users explore their imaging data and integrates with various viewer modules such as: d Stimulus Mapping module which allows users to map temporal information, such as stimulus or behavioral periods; e ROI Manager that can work with ROIs originating from a variety of sources, as shown in (b), and allows users to tag an unlimited variety of categorical information such as anatomical location, cell type, morphology, etc. for each ROI. f All data pertaining to an imaging session, i.e. the image sequence, calcium curves, ROIs, tags (annotations), stimulus mappings, and all other categorical information are packaged into a Project Sample and saved to the Project Dataset. g The samples within a Project Dataset can be interactively managed using the Project Browser. h–j Project Datasets, or sub-datasets, can be loaded into a flowchart to interactively perform downstream analysis. Simplified examples of how flowcharts can be used to h explore stimulus or behavioral responses, i analyze peak features (width, amplitude, slope etc.) or perform k-shape clustering and j perform hierarchical clustering. k Downstream analysis in flowcharts are integrated with various forms of highly interactive plots such as cross-correlation analysis. Many interactive plots are associated with a l Datapoint Tracer where users can click on individual datapoints to view the spatial location of the ROI that it originates from, along with all other data associated with that datapoint. m The Datapoint Tracer shown in (l) also lets users view the analysis history log for every datapoint in the form of an Analysis Graph.