# Automatic Neuronal Activity Segmentation in Fast Four Dimensional Spatio-Temporal Fluorescence Imaging using Bayesian Approach

arXiv: https://arxiv.org/abs/2512.19032 · HTML: https://mlsanity.com/paper/2512.19032/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 22 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation](https://mlsanity.com/paper/1808.01200/) (2018)
- [Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches](https://mlsanity.com/paper/1803.04386/) (2018)
- [Fast Online Deconvolution of Calcium Imaging Data](https://mlsanity.com/paper/1609.00639/) (2016)
- [SC-VAE: Sparse Coding-based Variational Autoencoder with Learned ISTA](https://mlsanity.com/paper/2303.16666/) (2023)
- [HNCcorr: A Novel Combinatorial Approach for Cell Identification in Calcium-Imaging Movies](https://mlsanity.com/paper/1703.01999/) (2017)
- [Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks](https://mlsanity.com/paper/1606.07372/) (2016)
- [Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks](https://mlsanity.com/paper/1707.06314/) (2017)
- [DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging](https://mlsanity.com/paper/1908.07957/) (2019)
- [A structured matrix factorization framework for large scale calcium imaging data analysis](https://mlsanity.com/paper/1409.2903/) (2014)
- [GSVD-NMF: Recovering Missing Features in Non-negative Matrix Factorization](https://mlsanity.com/paper/2408.08260/) (2024)

## Abstract (arXiv)

Fluorescence Microcopy Calcium Imaging is a fundamental tool to in-vivo record and analyze large scale neuronal activities simultaneously at a single cell resolution. Automatic and precise detection of behaviorally relevant neuron activity from the recordings is critical to study the mapping of brain activity in organisms. However a perpetual bottleneck to this problem is the manual segmentation which is time and labor intensive and lacks generalizability. To this end, we present a Bayesian Deep Learning Framework to detect neuronal activities in 4D spatio-temporal data obtained by light sheet microscopy. Our approach accounts for the use of temporal information by calculating pixel wise correlation maps and combines it with spatial information given by the mean summary image. The Bayesian framework not only produces probability segmentation maps but also models the uncertainty pertaining to active neuron detection. To evaluate the accuracy of our framework we implemented the test of reproducibility to assert the generalization of the network to detect neuron activity. The network achieved a mean Dice Score of 0.81 relative to the synthetic Ground Truth obtained by Otsu's method and a mean Dice Score of 0.79 between the first and second run for test of reproducibility. Our method successfully deployed can be used for rapid detection of active neuronal activities for behavioural studies.