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Deep learning multi-shot 3D localization microscopy using hybrid optical–electronic computing

Abstract

Current 3D localization microscopy approaches are fundamentally limited in their ability to image thick, densely labeled specimens. Here, we introduce a hybrid optical–electronic computing approach that jointly optimizes an optical encoder (a set of multiple, simultaneously imaged 3D point spread functions) and an electronic decoder (a neural-network-based localization algorithm) to optimize 3D localization performance under these conditions. With extensive simulations and biological experiments, we demonstrate that our deep-learning-based microscope achieves significantly higher 3D localization accuracy than existing approaches, especially in challenging scenarios with high molecular density over large depth ranges.

© 2021 Optical Society of America

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Supplementary Material (3)

NameDescription
Supplement 1       supplemental document
Visualization 1       (a,b) Captured sensor images of dense microspheres inside fixed 3D cells. (c,d) Maximum intensity projections of a deconvolved z-stack of the same cells overlaid with microspheres (magenta), nucleus (cyan) and cytoplasm (yellow). The two cells contai
Visualization 2       (a,b) Sensor images of a single frame of a time-lapse movie showing the dynamics of the cytoplasm of live cells. The scale bar represents 5 µm. (c) Composite image of cell nucleus, cytoplasm, and microspheres captured through one of our DOEs. (d) Est

Data availability

Data underlying the results presented in this Letter are not publicly available at this time but may be obtained from the authors upon reasonable request.

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