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

TL;DR

This paper introduces a Bayesian deep learning framework for automatic, accurate, and reproducible segmentation of neuronal activity in 4D fluorescence microscopy data, significantly reducing manual effort.

Contribution

The novel Bayesian approach integrates spatial and temporal information for neuronal activity detection, providing uncertainty modeling and high reproducibility in 4D imaging data.

Findings

Achieved a mean Dice Score of 0.81 on synthetic data.

Demonstrated reproducibility with a Dice Score of 0.79 between runs.

Enabled rapid, automated detection of neuronal activity for behavioral studies.

Abstract

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.