Deep self-supervised learning for computational adaptive optics in widefield microscopy

Iksung Kang; Qinrong Zhang; Na Ji

doi:10.1117/12.2658934

16 March 2023 Deep self-supervised learning for computational adaptive optics in widefield microscopy

Iksung Kang, Qinrong Zhang, Na Ji

Proceedings Volume 12388, Adaptive Optics and Wavefront Control for Biological Systems IX; 123880H (2023) https://doi.org/10.1117/12.2658934
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

Optical microscopy with adaptive optics (AO) allows high-resolution noninvasive imaging of subcellular structures in living organisms. As alternatives to hardware-based AO methods, supervised deep-learning approaches have recently been developed to estimate optical aberrations. However, these approaches are often limited in their generalizability due to discrepancies between training and imaging settings. Moreover, a corrective device is still required to compensate for aberrations in order to obtain high-resolution images. Here we describe a deep self-supervised learning approach for simultaneous aberration estimation and structural information recovery from a single 3D image stack acquired by widefield microscopy. The approach utilizes coordinate-based neural representations to represent highly complex structures. We experimentally validated our approach with directwavefront-sensing-based AO in the same samples and showed the approach is applicable to in vivo mouse brain imaging

Citation Download Citation

Iksung Kang, Qinrong Zhang, and Na Ji "Deep self-supervised learning for computational adaptive optics in widefield microscopy", Proc. SPIE 12388, Adaptive Optics and Wavefront Control for Biological Systems IX, 123880H (16 March 2023); https://doi.org/10.1117/12.2658934

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