Fluorescence microscopy has been a valuable tool in the field of biological science as it allows one to study the structure and interaction of protein complexes and organelles in living cells. However, conventional optical microscopy technique has been limited by a trade-off between spatiotemporal resolution, signal contrast, and photodamage to the biological samples. It means that an increase in spatial resolution or signal contrast comes at the cost of higher laser power, serial-scanning, or longer image acquisition time. Unfortunately, this leads to severe photobleaching and photodamage to the samples and/or limited throughput of imaging, which is highly challenging to be circumvented through only optical imaging technique. Therefore, one has turned to artificial intelligence (AI) in image processing, applying deep learning algorithms to different imaging modalities to overcome these traditional limitations in optical microscopy systems. Herein we present multiple strategies on how deep learning can be applied to solve challenging and fundamental problems in different fluorescence microscopy modalities. To do so, we present UNet-RCAN, a two-step deep learning network architecture based on a residual U-Net and residual channel attention network (RCAN) for image restoration. We demonstrate that UNet-RCAN achieves higher prediction accuracy compared to other state-of-the-art deep learning algorithms while maintaining the resolution of an output image compared to ground-truth data acquired with optical microscopes. We applied our method to three fluorescence imaging modalities. Firstly, we successfully demonstrate that UNet-RCAN can achieve up to two orders of magnitude acceleration in stimulated emission depletion (STED) imaging while maintaining super-resolution. This significant acceleration enables mitigation of photobleaching and photodamage by robust restoration of noisy 2D and 3D STED images from multiple targets as well as live-cell STED imaging of inner-mitochondrial dynamics with a ten-fold increase in the number of acquired frames compared to conventional STED microscopy. Secondly, we apply our approach in restoring high-resolution widefield deconvolution images of living cells with low light intensity and low photodamage. We show that the accuracy of deconvolution can significantly improve after image restoration with deep learning. Lastly, we show the application of UNet-RCAN in the resolution enhancement of single-shot volumetric imaging with a low numerical aperture objective lens.
If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu
Han, Kyu Young
Doctor of Philosophy (Ph.D.)
College of Optics and Photonics
Optics and Photonics
Optics and Photonics
Length of Campus-only Access
Doctoral Dissertation (Campus-only Access)
Ebrahimi, Vahid, "Deep Learning-Based Microscopy" (2023). Electronic Theses and Dissertations, 2020-. 1837.
Restricted to the UCF community until August 2024; it will then be open access.