The Menon Lab

Advancing Optics, Nanofabrication & Computation.



Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.


Journal article


R. Guo, Soren Nelson, Matthew J. Regier, M. Davis, E. Jorgensen, Jason Shepherd, R. Menon
Optics Express, 2021

Semantic Scholar DOI PubMed
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APA   Click to copy
Guo, R., Nelson, S., Regier, M. J., Davis, M., Jorgensen, E., Shepherd, J., & Menon, R. (2021). Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging. Optics Express.


Chicago/Turabian   Click to copy
Guo, R., Soren Nelson, Matthew J. Regier, M. Davis, E. Jorgensen, Jason Shepherd, and R. Menon. “Scan-Less Machine-Learning-Enabled Incoherent Microscopy for Minimally-Invasive Deep-Brain Imaging.” Optics Express (2021).


MLA   Click to copy
Guo, R., et al. “Scan-Less Machine-Learning-Enabled Incoherent Microscopy for Minimally-Invasive Deep-Brain Imaging.” Optics Express, 2021.


BibTeX   Click to copy

@article{r2021a,
  title = {Scan-less machine-learning-enabled incoherent microscopy for minimally-invasive deep-brain imaging.},
  year = {2021},
  journal = {Optics Express},
  author = {Guo, R. and Nelson, Soren and Regier, Matthew J. and Davis, M. and Jorgensen, E. and Shepherd, Jason and Menon, R.}
}

Abstract

Deep-brain microscopy is strongly limited by the size of the imaging probe, both in terms of achievable resolution and potential trauma due to surgery. Here, we show that a segment of an ultra-thin multi-mode fiber (cannula) can replace the bulky microscope objective inside the brain. By creating a self-consistent deep neural network that is trained to reconstruct anthropocentric images from the raw signal transported by the cannula, we demonstrate a single-cell resolution (< 10μm), depth sectioning resolution of 40 μm, and field of view of 200 μm, all with green-fluorescent-protein labelled neurons imaged at depths as large as 1.4 mm from the brain surface. Since ground-truth images at these depths are challenging to obtain in vivo, we propose a novel ensemble method that averages the reconstructed images from disparate deep-neural-network architectures. Finally, we demonstrate dynamic imaging of moving GCaMp-labelled C. elegans worms. Our approach dramatically simplifies deep-brain microscopy.


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