
==== Front
bioRxiv
BIORXIV
bioRxiv
2692-8205
Cold Spring Harbor Laboratory

10.1101/2024.08.23.609461
preprint
2
Article
CRISP: Correlation-Refined Image Segmentation Process
Briggs Jennifer K. http://orcid.org/0000-0002-8737-2215

Jin Erli http://orcid.org/0009-0002-9410-9738

Merrins Matthew J. http://orcid.org/0000-0003-1599-9227

Benninger Richard K.P. http://orcid.org/0000-0002-5063-6096

26 8 2024
2024.08.23.609461https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.
http://biorxiv.org/lookup/doi/10.1101/2024.08.23.609461
nihpp-2024.08.23.609461.pdf
Abstract

Calcium imaging offers the ability to observe cellular activities in real-time across various contexts. However, the manual outlining of cells in calcium imaging data introduces potential errors. This paper introduces the Correlation-Refined Image Segmentation Process (CRISP), an automated algorithm designed to enhance the accuracy of cell mask refinement and to assist in the identification of cell boundaries. CRISP leverages intracell correlations to refine manually drawn masks and automate the detection of the largest cell area that contains only pixels from within the cell. The algorithm not only enhances the precision of calcium trace data but also improves the reliability of functional network analyses.
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pmc
