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

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.

Figures (9)

• Figure 1: Overview of Astro-BEATS in the context of mSCTs analysis. Raw Ca$^{2+}$-imaging videos are processed using Astro-BEATS, an unsupervised source-finding algorithm used to generate segmentation masks of transients in these videos. The RHTrht_clark is used to generate a dendritic foreground mask, a mask of pixels that contain the foreground GCaMP fluorescence signal (top row, described in \ref{['sec:rht']}). A dynamic estimate of the GCaMP6f fluorescence signal in the dendrites is used to estimate the average foreground in each frame. We take the difference image between this weighted foreground and our raw Ca$^{2+}$ video to isolate mSCTs from the foreground (middle row, described in \ref{['sec:decompose']}). Finally, density-based spatial clustering of applications with noise (DBSCAN) source-finding is used on pixels in the difference image to identify and segment transients (bottom row, described in \ref{['sec:astrobeats_sources']}).
• Figure 1: Large-scale fluctuation in the brightness of GCaMP6f on the dendritic shaft. The dendrite appears brighter at $t=0~\mathrm{s}$ compared to $t=30~\mathrm{s}$. These brightness fluctuations are accounted for in Astro-BEATS by taking a separate time-dependent background estimate for each frame.
• Figure 2: A) Precision, Recall and F$_1$ Score for mSCT detection by Astro-BEATS, IDT and AQuA (N=9 videos). The center line represents the median value while, the boxes represents the 2$^{nd}$ and 3$^{rd}$ quartile range. Whiskers represent the maximum and minimum value and raw data points are plotted beside each box plot. B) Comparison of transients detected by Astro-BEATS compared to manual ground truth annotations (True positive (TP); gray, FN; red, FP; blue). Volume ($V$) is expressed in units of voxels (1 voxel = 0.00256 $\mu$m$^2$ms) and maximum brightness ($B_\mathrm{max}$) is obtained from the transient signal map ($\sigma$). See \ref{['table:aperture properties']} for descriptions of these variables. C) Comparison of transients detected by Astro-BEATS and IDT (detected by both algorithms: green, detected by Astro-BEATS only: grey, detected by IDT only: black cross, missed by both algorithms: red). D) Example images of out-of-focus, synaptic and dendritic transients during their temporal maximum. We also show the image 1s before the temporal peak of each transient. E) The $V$ and $B_\mathrm{max}$ of each transient types. F) The proportion of manually annotated Ca$^{2+}$ transients recovered by Astro-BEATS and IDT, for various types of Ca$^{2+}$ transients.
• Figure 2: A) Optimization of the DBSCAN parameters. The range of DBSCAN parameters that yield good F$_1$ scores are shown in \ref{['table:dbscan_params']}. We find that for a range of $400< \mathrm{Min}_\mathrm{pts} < 700$ and $5\sigma < \mu < 9\sigma$, we obtain F$_1 > 0.7$, which we consider to be acceptable. B) Precision, Recall and F$_1$ scores obtained by finding the DBSCAN parameters that achieve the highest F$_1$ score for one video, and applied over the remaining 9 test videos.
• Figure 3: A) Segmentation DiCE scores calculated over manually segmented transients, using expert segmentation masks as ground truth. Since there is variation between expert annotations, we use inter-expert agreement DiCE scores as a baseline of comparison for segmentation performance (\ref{['sec:event_segmentation_compare']}). When comparing segmentation DiCE score across transient types described in \ref{['sec:dataset_classes']}, we find that Astro-BEATS has comparable DiCE scores to IDT for synaptic transients, while having improved DiCE scores for dendritic and out-of-focus events. C) Example Annotations generated by Astro-BEATS compared to expert annotations. We chose example transients with Astro-BEATS DiCE segmentation scores closest to the median.