title: SIMA: Python software for analysis of dynamic fluorescence imaging data.
abstract: Fluorescence imaging is a powerful method for monitoring dynamic signals in the nervous system. However, analysis of dynamic fluorescence imaging data remains burdensome, in part due to the shortage of available software tools. To address this need, we have developed SIMA, an open source Python package that facilitates common analysis tasks related to fluorescence imaging. Functionality of this package includes correction of motion artifacts occurring during in vivo imaging with laser-scanning microscopy, segmentation of imaged fields into regions of interest (ROIs), and extraction of signals from the segmented ROIs. We have also developed a graphical user interface (GUI) for manual editing of the automatically segmented ROIs and automated registration of ROIs across multiple imaging datasets. This software has been designed with flexibility in mind to allow for future extension with different analysis methods and potential integration with other packages. Software, documentation, and source code for the SIMA package and ROI Buddy GUI are freely available at http://www.losonczylab.org/sima/.
authors.affiliation: Department of Neuroscience, Columbia University in the City of New York New York, NY, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Kaifosh
authors.affiliation: Department of Neuroscience, Columbia University in the City of New York New York, NY, USA.
authors.forename: Jeffrey D
authors.initials: JD
authors.lastname: Zaremba
authors.affiliation: Department of Neuroscience, Columbia University in the City of New York New York, NY, USA.
authors.forename: Nathan B
authors.initials: NB
authors.lastname: Danielson
authors.affiliation: Department of Neuroscience, Columbia University in the City of New York New York, NY, USA.
authors.forename: Attila
authors.initials: A
authors.lastname: Losonczy
keywords: Python language; analysis software; calcium imaging; fluorescence imaging; in vivo GECI imaging; motion correction; multi-photon microscopy; segmentation