Ms. Vy Bui Profile

Vy Bui

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Publications (5)

SPIE Journal Paper | 29 September 2020 Open Access

Virtual organelle self-coding for fluorescence imaging via adversarial learning

Thanh Nguyen, Vy Bui, Anh Thai, Van Lam, Christopher Raub, Lin-Ching Chang, Georges Nehmetallah

JBO, Vol. 25, Issue 09, 096009, (September 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.9.096009

KEYWORDS: Luminescence, Data modeling, Microscopy, Phase contrast, Tolerancing, Proteins, Instrument modeling, Algorithm development, Performance modeling, Image segmentation

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Significance: Our study introduces an application of deep learning to virtually generate fluorescence images to reduce the burdens of cost and time from considerable effort in sample preparation related to chemical fixation and staining. Aim: The objective of our work was to determine how successfully deep learning methods perform on fluorescence prediction that depends on structural and/or a functional relationship between input labels and output labels. Approach: We present a virtual-fluorescence-staining method based on deep neural networks (VirFluoNet) to transform co-registered images of cells into subcellular compartment-specific molecular fluorescence labels in the same field-of-view. An algorithm based on conditional generative adversarial networks was developed and trained on microscopy datasets from breast-cancer and bone-osteosarcoma cell lines: MDA-MB-231 and U2OS, respectively. Several established performance metrics—the mean absolute error (MAE), peak-signal-to-noise ratio (PSNR), and structural-similarity-index (SSIM)—as well as a novel performance metric, the tolerance level, were measured and compared for the same algorithm and input data. Results: For the MDA-MB-231 cells, F-actin signal performed the fluorescent antibody staining of vinculin prediction better than phase-contrast as an input. For the U2OS cells, satisfactory metrics of performance were archieved in comparison with ground truth. MAE is <0.005, 0.017, 0.012; PSNR is >40 / 34 / 33 dB; and SSIM is >0.925 / 0.926 / 0.925 for 4′,6-diamidino-2-phenylindole/hoechst, endoplasmic reticulum, and mitochondria prediction, respectively, from channels of nucleoli and cytoplasmic RNA, Golgi plasma membrane, and F-actin. Conclusions: These findings contribute to the understanding of the utility and limitations of deep learning image-regression to predict fluorescence microscopy datasets of biological cells. We infer that predicted image labels must have either a structural and/or a functional relationship to input labels. Furthermore, the approach introduced here holds promise for modeling the internal spatial relationships between organelles and biomolecules within living cells, leading to detection and quantification of alterations from a standard training dataset.

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SPIE Journal Paper | 18 February 2020 Open Access

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

Van Lam, Thanh Nguyen, Vy Bui, Byung Min Chung, Lin-Ching Chang, George Nehmetallah, Christopher Raub

JBO, Vol. 25, Issue 02, 026002, (February 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.2.026002

KEYWORDS: Cancer, Machine learning, Breast cancer, Binary data, Digital holography, Phase imaging, Holography, Data modeling, Microscopy, Oncology

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SPIE Journal Paper | 26 April 2018

Computational optical tomography using 3-D deep convolutional neural networks

Thanh Nguyen, Vy Bui, George Nehmetallah

OE, Vol. 57, Issue 04, 043111, (April 2018) https://doi.org/10.1117/12.10.1117/1.OE.57.4.043111

KEYWORDS: 3D modeling, 3D image processing, Refraction, Optical tomography, Diffraction, Spatial light modulators, Image processing, Charge-coupled devices, Visualization, Convolutional neural networks

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Proceedings Article | 22 May 2015 Paper

Secure communication systems using synchronized Lorenz strange attractor on reconfigurable hardware

Marc Sepantaie, Vy Bui, Esam El-Araby, Amir Sepantaie, Georges Nehmatallah, Nader Namazi

Proceedings Volume 9478, 947802 (2015) https://doi.org/10.1117/12.2177589

KEYWORDS: Telecommunications, Binary data, Discrete wavelet transforms, Signal to noise ratio, Prototyping, Receivers, Field programmable gate arrays, Wavelets, Transmitters, Finite impulse response filters

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Proceedings Article | 9 June 2014 Paper

Detecting binary non-return-to-zero data in free-space optical communication systems using FPGAs

Vy Bui, Lan Tran, Esam El-Araby, Nader Namazi

Proceedings Volume 9080, 90801G (2014) https://doi.org/10.1117/12.2049850

KEYWORDS: Binary data, Free space optics, Signal to noise ratio, Wavelets, Field programmable gate arrays, Optical filters, Prototyping, Telecommunications, Free space optical communications, Interference (communication)

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