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Spatio-temporal feature learning with reservoir computing for T-cell segmentation in live-cell [Formula: see text]
Fatemeh Hadaeghi1, Björn-Philipp Diercks2, Daniel Schetelig1,3
1Department of Computational Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Martinistrasse 52, 20246 Hamburg, Germany.
Scientific Reports
|April 16, 2021
Summary
Reservoir computing offers efficient and temporally consistent segmentation for live-cell imaging data, outperforming deep learning models in accuracy and speed. This approach enhances the analysis of calcium signaling events in T-cells.
Area of Science:
- Cellular and Molecular Biology
- Biophysics
- Computational Biology
Background:
- High-resolution live-cell imaging allows subcellular localization of early calcium signaling events in T-cells.
- Analyzing large datasets from live-cell imaging requires efficient, automated image processing pipelines, particularly for cell segmentation.
- Automated segmentation is challenging due to temporal fluctuations, low signal-to-noise ratio, and photo-bleaching in live-cell imaging data.
Purpose of the Study:
- To propose and evaluate a reservoir computing (RC) framework for efficient and temporally consistent segmentation of live-cell cytosolic calcium imaging data.
- To compare the performance of RC models against standard deep learning models like U-Net and convolutional Long Short-Term Memory (LSTM).
- To assess the utility of RC in differentiating T-cells and beads in complex imaging scenarios.
Main Methods:
- Development of a reservoir computing (RC) framework for image segmentation.
- Experimental validation using Jurkat T-cells and anti-CD3 coated beads for T-cell activation.
- Comparative analysis of RC models against U-Net and convolutional LSTM models on various segmentation tasks.
Main Results:
- RC models achieve segmentation accuracy comparable to deep learning models for cell-only segmentation.
- RC demonstrates improved temporal segmentation consistency over U-Net.
- RC outperforms U-Net in two-emission wavelength image segmentation and T-cell/bead differentiation.
- RC performs comparably to convolutional LSTM for single-emission wavelength T-cell/bead segmentation and differentiation.
- RC models exhibit significantly fewer parameters and reduced training time compared to baseline models.
Conclusions:
- Reservoir computing provides an efficient and effective framework for automated segmentation in challenging live-cell calcium imaging data.
- RC offers a computationally advantageous alternative to deep learning methods, with comparable or superior performance in specific segmentation tasks.
- The developed RC framework enhances the analysis of T-cell activation and calcium signaling dynamics.
