Cargando…

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

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 f...

Descripción completa

Detalles Bibliográficos
Autores principales: Moghnieh, Hussein, Kamran, Sharif Amit, Hossain, Khondker Fariha, Kuol, Nyanbol, Riar, Sarah, Bartlett, Allison, Tavakkoli, Alireza, Baker, Salah A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674926/
https://www.ncbi.nlm.nih.gov/pubmed/36595928
http://dx.doi.org/10.1016/j.xpro.2022.101852
Descripción
Sumario: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).(1)