Ms. Lynn Lin Profile

Lynn Lin

at ICFO - Institut de Ciències Fotòniques

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Proceedings Article | 3 October 2022 Presentation + Paper

Deep learning enhanced bioluminescence microscopy

Luis Felipe Morales-Curiel, Gustavo Castro-Olvera, Adriana Gonzalez, Lynn Lin, Montserrat Porta-de-la-Riva, Diego Ramallo, Pablo Loza-Alvarez, Michael Krieg

Proceedings Volume 12204, 122040B (2022) https://doi.org/10.1117/12.2632717

KEYWORDS: Microscopy, Bioluminescence, Neural networks, Luminescence, Denoising, Reconstruction algorithms, Image processing

Read Abstract +

In fluorescence microscopy, an external source of excitation light is required for photon emission and thereby sample visualization. Even though fluorescence imaging has provided a paradigm shift for cell biology and other disciplines, the sample might suffer due to high excitation light intensities, and spurious signals originating from autofluorescence. Bioluminescence imaging, on the contrary, does not need an external source of light for photon emission and visualization, bypassing the effects of autofluorescence, phototoxicity and photobleaching. This renders bioluminescence microscopy as an ideal tool for long term imaging. A major limitation of bioluminescence, compared to fluorescence imaging, is the low quantum yield of the bioluminescent proteins, which requires long exposure times and large collecting wells. Here, we work towards universal tools to overcome the main limitations of bioluminescence imaging: low signal/noise (SNR) imaging. To enhance spatiotemporal resolution, we have designed an optimized setup that boosts the optical efficiency and combine the photon starved, low SNR output with deep learning based content aware reconstruction methods. We trained a UNet architecture neural network with augmented fluorescent experimental data to denoise low SNR bioluminescent images. In addition, we trained a subpixel convolutional network with synthetic light field data to perform 3D reconstruction from a single photographic exposure without the presence of autofluorescence. Furthermore, we compare the reconstruction time and quality improvement with classical deconvolution methods.

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