Augmented multiplexed asymmetric-detection time-stretch optical microscopy by generative deep learning

Michelle C. K. Lo; Kelvin C. M. Lee; Dickson M. D. Siu; Edmund Y. Lam; Kevin K. Tsia

doi:10.1117/12.2582985

5 March 2021 Augmented multiplexed asymmetric-detection time-stretch optical microscopy by generative deep learning

Michelle C. K. Lo, Kelvin C. M. Lee, Dickson M. D. Siu, Edmund Y. Lam, Kevin K. Tsia

Proceedings Volume 11654, High-Speed Biomedical Imaging and Spectroscopy VI; 1165410 (2021) https://doi.org/10.1117/12.2582985
Event: SPIE BiOS, 2021, Online Only

Abstract

We report a robust method based on generative deep learning to reconstruct quantitative phase image (QPI). By employing multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM), we simultaneously captured multiple intensity image contrasts of the same cell in microfluidic flow, revealing different phase-gradient orientations at high throughput (10,000 cells/sec). Using conditional generative adversarial networks (cGAN), we performed a systematic analysis of how different orientations of the phase-gradient contrasts and their combinations influence the QPI prediction performance, which overall general achieves a high similarity (structural similarity index > 0.91) and low error rate (mean squared error < 0.01).

Conference Presentation

Citation Download Citation

Michelle C. K. Lo, Kelvin C. M. Lee, Dickson M. D. Siu, Edmund Y. Lam, and Kevin K. Tsia "Augmented multiplexed asymmetric-detection time-stretch optical microscopy by generative deep learning", Proc. SPIE 11654, High-Speed Biomedical Imaging and Spectroscopy VI, 1165410 (5 March 2021); https://doi.org/10.1117/12.2582985

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