Software for segmenting and quantifying calcium signals using multi-scale generative adversarial networks.

Journal: STAR protocols
PMID: 36595928

Abstract

Cellular calcium fluorescence imaging utilized to study cellular behaviors typically results in large datasets and a profound need for standardized and accurate analysis methods. Here, we describe open-source software (4SM) to overcome these limitations using an automated machine learning pipeline for subcellular calcium signal segmentation of spatiotemporal maps. The primary use of 4SM is to analyze spatiotemporal maps of calcium activities within cells or across multiple cells. For complete details on the use and execution of this protocol, please refer to Kamran et al. (2022)..

Authors

  • Hussein Moghnieh
    Department of Electrical and Computer Engineering, McGill University, Montréal, QC H3A 0E9, Canada.
  • Sharif Amit Kamran
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.
  • Khondker Fariha Hossain
    Department of Computer Science, Deakin University, Melbourne, VIC, 3217, Australia.
  • Nyanbol Kuol
    Department of Physiology and Cell Biology, University of Nevada, School of Medicine, Anderson Medical Building MS352, Reno, NV 89557, USA.
  • Sarah Riar
    Department of Physiology and Cell Biology, University of Nevada, School of Medicine, Anderson Medical Building MS352, Reno, NV 89557, USA.
  • Allison Bartlett
    Department of Physiology and Cell Biology, University of Nevada, School of Medicine, Anderson Medical Building MS352, Reno, NV 89557, USA.
  • Alireza Tavakkoli
    Department of Computer Science and Engineering, University of Nevada School of Medicine, Reno, NV 89557, USA.
  • Salah A Baker
    Department of Physiology and Cell Biology, University of Nevada School of Medicine, Reno, NV 89557, USA. Electronic address: sabubaker@med.unr.edu.

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