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1. Deep learning 2D and 3D optical sectioning microscopy using cross-modality Pix2Pix cGAN image translation

   https://doi.org/10.1364/BOE.439894  

   Zhuge, Huimin; Summa, Brian; Hamm, Jihun; Brown, J_Quincy (November 2021, Biomedical Optics Express)

   Structured illumination microscopy (SIM) reconstructs optically-sectioned images of a sample from multiple spatially-patterned wide-field images, but the traditional single non-patterned wide-field images are more inexpensively obtained since they do not require generation of specialized illumination patterns. In this work, we translated wide-field fluorescence microscopy images to optically-sectioned SIM images by a Pix2Pix conditional generative adversarial network (cGAN). Our model shows the capability of both 2D cross-modality image translation from wide-field images to optical sections, and further demonstrates potential to recover 3D optically-sectioned volumes from wide-field image stacks. The utility of the model was tested on a variety of samples including fluorescent beads and fresh human tissue samples. 

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2. Subcellular three-dimensional imaging deep through multicellular thick samples by structured illumination microscopy and adaptive optics

   https://doi.org/10.1038/s41467-021-23449-6  

   Lin, Ruizhe; Kipreos, Edward T.; Zhu, Jie; Khang, Chang Hyun; Kner, Peter (May 2021, Nature Communications)

   Abstract Structured Illumination Microscopy enables live imaging with sub-diffraction resolution. Unfortunately, optical aberrations can lead to loss of resolution and artifacts in Structured Illumination Microscopy rendering the technique unusable in samples thicker than a single cell. Here we report on the combination of Adaptive Optics and Structured Illumination Microscopy enabling imaging with 150 nm lateral and 570 nm axial resolution at a depth of 80 µm throughCaenorhabditis elegans. We demonstrate that Adaptive Optics improves the three-dimensional resolution, especially along the axial direction, and reduces artifacts, successfully realizing 3D-Structured Illumination Microscopy in a variety of biological samples. 

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3. Freeform Illuminator for Computational Microscopy

   https://doi.org/10.34133/icomputing.0015  

   Song, Pengming; Wang, Tianbo; Jiang, Shaowei; Guo, Chengfei; Wang, Ruihai; Yang, Liming; Zhou, You; Zheng, Guoan (January 2023, Intelligent Computing)

   Programmable illumination control is essential for many computational microscopy techniques. Conventional light source array is often arranged on a fixed grid of a planar surface for providing programmable sample illumination. Here, we report the development of a freeform illuminator that can be arranged at arbitrary 2-dimensional or 3-dimensional (3D) surface structures for computational microscopy. The freeform illuminator can be designed in a small form factor with a dense light source arrangement in 3D. It can be placed closer to the sample for providing angle-varied illumination with higher optical flux and smaller angular increment. With the freeform illuminators, we develop a calibration process using a low-cost Raspberry-Pi image sensor coated with a monolayer of blood cells. By tracking the positional shift of the blood-cell diffraction patterns at 2 distinct regions of the coded sensor, we can infer the 3D positions of the light source elements in a way similar to the stereo vision reconstruction approach. To demonstrate the applications for computational microscopy, we validate the freeform illuminators for Fourier ptychographic microscopy, 3D tomographic imaging, and on-chip microscopy. We also present a longitudinal study by tracking the growth of live bacterial cultures over a large field of view. The reported freeform illuminators and the related calibration process offer flexibilities and extended scope for imaging innovations in computational microscopy. 

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4. Extended depth-of-field light-sheet microscopy improves imaging of large volumes at high numerical aperture

   https://doi.org/10.1063/5.0101426  

   Keomanee-Dizon, Kevin; Jones, Matt; Luu, Peter; Fraser, Scott_E; Truong, Thai_V (October 2022, Applied Physics Letters)

   Light-sheet microscopes must compromise among field of view, optical sectioning, resolution, and detection efficiency. High-numerical-aperture (NA) detection objective lenses provide higher resolution, but their narrow depth of field inefficiently captures the fluorescence signal generated throughout the thickness of the illumination light sheet when imaging large volumes. Here, we present ExD-SPIM (extended depth-of-field selective-plane illumination microscopy), an improved light-sheet microscopy strategy that solves this limitation by extending the depth of field (DOF) of high-NA detection objectives to match the thickness of the illumination light sheet. This extension of the DOF uses a phase mask to axially stretch the point-spread function of the objective lens while largely preserving lateral resolution. This matching of the detection DOF to the illumination-sheet thickness increases the total fluorescence collection, reduces the background, and improves the overall signal-to-noise ratio (SNR), as shown by numerical simulations, imaging of bead phantoms, and imaging living animals. In comparison to conventional light sheet imaging with low-NA detection that yields equivalent DOF, the results show that ExD-SPIM increases the SNR by more than threefold and dramatically reduces the rate of photobleaching. Compared to conventional high-NA detection, ExD-SPIM improves the signal sensitivity and volumetric coverage of whole-brain activity imaging, increasing the number of detected neurons by over a third. 

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5. Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images

   Rao, Bovey Y; Peterson, Alexis M; Kandror, Elena K; Herrlinger, Stephanie; Losonczy, Attila; Paninski, Liam; Rizvi, Abbas H; Varol, Erdem (September 2021, Medical Image Computing and Computer Assisted Intervention – MICCAI 2021)

   de Bruijne, M. (Ed.)

   Spatial transcriptomics techniques such as STARmap [15] enable the subcellular detection of RNA transcripts within complex tissue sections. The data from these techniques are impacted by optical microscopy limitations, such as shading or vignetting effects from uneven illumination during image capture. Downstream analysis of these sparse spatially resolved transcripts is dependent upon the correction of these artefacts. This paper introduces a novel non-parametric vignetting correction tool for spatial transcriptomic images, which estimates the illumination field and background using an efficient iterative sliced histogram normalization routine. We show that our method outperforms the state-of-the-art shading correction techniques both in terms of illumination and background field estimation and requires fewer input images to perform the estimation adequately. We further demonstrate an important downstream application of our technique, showing that spatial transcriptomic volumes corrected by our method yield a higher and more uniform gene expression spot-calling in the rodent hippocampus. Python code and a demo file to reproduce our results are provided in the supplementary material and at this github page: https://github.com/BoveyRao/Non-parametric-vc-for-sparse-st. 

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