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. 2023 Sep 13;475(11):1283–1300. doi: 10.1007/s00424-023-02855-3

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© The Author(s) 2023

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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Temporal correlation–based ROI detection. (A) ROIs obtained using CoRoDe with different correlation thresholds (t_corr) as well as watershed transform. Correlation thresholds were set to 0.75, 0.5, and 0.25. (B) Direct comparison of automatically extracted ROIs to a manually evaluated ground truth. The obtained ROIs (pink) were contrasted to the manually extracted maximal extent of the fluorescence event (dashed blue line). (C) Fluorescence profiles from the highlighted ROIs showed the influence of accurate segmentation on resulting peak amplitudes. Transients of ROIs obtained by watershed segmentation exhibit too small peak amplitudes, while transient peaks of undersized ROIs were too high (arrows). (D) ROIs obtained using CoRoDe showed significantly less difference from ground truth, compared to watershed transform. (E) Integrated fluorescence profiles from ROIs with appropriate t_corr show no difference in SNR, compared to ground truth, whereas profiles from ROIs obtained using a watershed transform showed a significantly reduced SNR