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Robust self-supervised denoising of voltage imaging data using CellMincer.
Brice Wang1, Tianle Ma1,2, Theresa Chen3,4
1Data Sciences Platform (DSP), Broad Institute of MIT and Harvard, Cambridge, MA USA.
Npj Imaging
|December 9, 2024
Summary
CellMincer, a new deep learning tool, significantly improves voltage imaging by reducing noise. This method enhances neuronal activity detection and analysis, offering a substantial gain in signal-to-noise ratio for clearer insights.
Area of Science:
- Neuroscience
- Biophysics
- Computational Biology
Background:
- Voltage imaging is crucial for neuroscience but limited by low signal-to-noise ratios (SNR).
- Existing denoising methods struggle with the complex dynamics of voltage imaging data.
- Deep learning approaches have not fully addressed the specific challenges of this technique.
Purpose of the Study:
- Introduce CellMincer, a novel self-supervised deep learning method for denoising voltage imaging data.
- Improve SNR and enhance the detection of neuronal activity.
- Provide a robust tool for analyzing complex neural dynamics.
Main Methods:
- Developed CellMincer, a self-supervised deep learning model using masked pixel prediction.
- Conditioned the denoiser on spatiotemporal auto-correlations to capture long-range dependencies.
- Utilized a physics-based simulation framework for data generation and model optimization.
Main Results:
- CellMincer achieved a 3-fold SNR gain through spatiotemporal auto-correlation conditioning.
- Demonstrated state-of-the-art performance on simulated and real datasets, outperforming existing methods.
- Showcased significant improvements in neuronal segmentation, peak detection, and functional phenotype identification.
Conclusions:
- CellMincer offers substantial noise reduction and improved signal fidelity in voltage imaging.
- The method consistently enhances SNR by 0.5-2.9 dB and reduces variability by 17-55%.
- CellMincer represents a significant advancement for analyzing neuronal activity with voltage imaging.
