Ca2+ transient detection and segmentation with the Astronomically motivated algorithm for Background Estimation And Transient Segmentation (Astro-BEATS)

TL;DR

Astro-BEATS is an innovative algorithm inspired by astronomical transient detection techniques, designed to accurately identify and segment calcium transients in neuronal imaging data, outperforming existing threshold-based methods.

Contribution

The paper introduces Astro-BEATS, a novel astronomy-inspired algorithm for calcium transient detection that enhances segmentation accuracy and robustness in fluorescence microscopy.

Findings

Astro-BEATS outperforms current threshold-based detection methods.

Segmentation masks generated by Astro-BEATS improve deep learning training.

The algorithm is fast and adaptable to new datasets without re-optimization.

Abstract

Fluorescence-based Ca$^{2+}$-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. Detecting 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. We 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 Ca$^{2+}$-imaging videos. Astro-BEATS outperforms current threshold-based approaches for synaptic Ca$^{2+}$ transient detection and segmentation. The produced segmentation masks can be used to train a supervised deep learning algorithm for improved synaptic Ca$^{2+}$ transient detection in Ca$^{2+}$-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.