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bioRxiv · 10.64898/2026.03.13.711411

Calcium transient detection and segmentation with the astronomically motivated algorithm for background estimation and transient segmentation (Astro-BEATS)

Fan, B.·Bilodeau, A.·Beaupre, F.·Wiesner, T.·Gagne, C.·Lavoie-Cardinal, F.·Hlozek, R.
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Abstract

SignificanceFluorescence-based Ca2+-imaging is a powerful tool for studying localized neuronal activity, including miniature Synaptic Calcium Transients, providing real-time insights into synaptic activity. These transients induce only subtle changes in the fluorescence signal, often barely above baseline, which poses a significant challenge for automated synaptic transient detection and segmentation. AimDetecting astronomical transients similarly requires efficient algorithms that will remain robust over a large field of view with varying noise properties. We leverage techniques used in astronomical transient detection for miniature Synaptic Calcium Transient detection in fluorescence microscopy. ApproachWe present Astro-BEATS, an automatic miniature Synaptic Calcium Transient segmentation algorithm that incorporates image estimation and source-finding techniques used in astronomy and designed for Ca2+-imaging videos. Astro-BEATS uses the Rolling Hough Transform filament detector to construct an estimate of the expected (transient-free) fluorescence signal of both the dendritic foreground and the background. Subtracting this baseline signal yields difference images displaying transient signals. We use Density-Based Spatial Clustering of Applications with Noise to find sources clustered in spatial and temporal space. ResultsAstro-BEATS outperforms current threshold-based approaches for synaptic Ca2+ transient detection and segmentation. The produced segmentation masks can be used to train a supervised deep learning algorithm for improved synaptic Ca2+ transient detection in Ca2+-imaging data. The speed of Astro-BEATS and its applicability to previously unseen datasets without re-optimization makes it particularly useful for generating training datasets for deep learning-based approaches. ConclusionAstro-BEATS greatly reduces the time needed for the annotation of synaptic Ca2+ transient and removes the significant overhead of human expert annotation, enabling consistent analysis of new Ca2+-imaging datasets.

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Fan, B., Bilodeau, A., Beaupre, F., Wiesner, T., Gagne, C., Lavoie-Cardinal, F., Hlozek, R.. 2026-03-17. Calcium transient detection and segmentation with the astronomically motivated algorithm for background estimation and transient segmentation (Astro-BEATS). https://doi.org/10.64898/2026.03.13.711411

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