Two-photon voltage imaging denoising by self-supervised learning

Chang Liu; Jelena Platisa; Xin Ye; Allison M. Ahrens; Ichun Anderson Chen; Ian G. Davison; Vincent A. Pieribone; Jerry L. Chen; Lei Tian

doi:10.1117/12.2648122

14 March 2023 Two-photon voltage imaging denoising by self-supervised learning

Chang Liu, Jelena Platisa, Xin Ye, Allison M. Ahrens, Ichun Anderson Chen, Ian G. Davison, Vincent A. Pieribone, Jerry L. Chen, Lei Tian

Author Affiliations +

Proceedings Volume 12365, Neural Imaging and Sensing 2023; 1236505 (2023) https://doi.org/10.1117/12.2648122
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

High-speed low-light two-photon voltage imaging is an emerging tool to simultaneously monitor neuronal activity from a large number of neurons. However, shot noise dominates pixel-wise measurements and the neuronal signals are difficult to be identified in the single-frame raw measurement. We developed a self-supervised deep learning framework for voltage imaging denoising, DeepVID, without the need for any high-SNR measurements. DeepVID infers the underlying fluorescence signal based on independent temporal and spatial statistics of the measurement that is attributable to shot noise. DeepVID achieved a 15-fold improvement in SNR when comparing denoised and raw image data.

Conference Presentation

Citation Download Citation

Chang Liu, Jelena Platisa, Xin Ye, Allison M. Ahrens, Ichun Anderson Chen, Ian G. Davison, Vincent A. Pieribone, Jerry L. Chen, and Lei Tian "Two-photon voltage imaging denoising by self-supervised learning", Proc. SPIE 12365, Neural Imaging and Sensing 2023, 1236505 (14 March 2023); https://doi.org/10.1117/12.2648122

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