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Deep learning multi-shot 3D localization microscopy using hybrid optical electronic computing
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DOI：10.1364/OL.441743.png)
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Abstract
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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. (C) 2021 Optical Society of America
Keywords:
DIFFRACTION-LIMIT
PARTICLE TRACKING
SUPERRESOLUTION
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3.3
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4.0W
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Cited Papers
ShareSaveThree-dimensional sub-100 nm resolution fluorescence microscopy of thick samples.png)
NATURE METHODS
NATURE METHODSIF32.1
PREAI
ShareSaveA convex 3D deconvolution algorithm for low photon count fluorescence imagingSCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSaveSub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)NATURE METHODSOAAI
NATURE METHODSIF32.1
OAAIShareSaveShareSaveSuper-resolution fight club: assessment of 2D and 3D single-molecule localization microscopy softwareNATURE METHODSOAAI
NATURE METHODSIF32.1
OAAIShareSavePrecise Three-Dimensional Scan-Free Multiple-Particle Tracking over Large Axial Ranges with Tetrapod Point Spread FunctionsNANO LETTERSOAAI
NANO LETTERSIF9.1
OAAIShareSave
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