Dr. Thanh C. Nguyen Profile

Dr. Thanh C. Nguyen

Postdoctoral Research Associate

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Publications (11)

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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Proceedings Article | 22 April 2020 Presentation + Paper

Portable flow device using Fourier ptychography microscopy and deep learning for detection in biosignatures

Thanh Nguyen, Shahid Aslam, Dina Bower, Jennifer Eigenbrode, Nicolas Gorius, Tilak Hewagama, Laddawan Miko, George Nehmetallah

Proceedings Volume 11401, 114010H (2020) https://doi.org/10.1117/12.2557316

KEYWORDS: Data modeling, Microscopes, Reconstruction algorithms, Image resolution, Microscopy, Spatial resolution, Liquids, Light emitting diodes, Cameras, Optical microscopes

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Proceedings Article | 21 April 2020 Presentation + Paper

A generalized approach for digital holographic recording and reconstruction

Brad Bazow, Thuc Phan, Thanh Nguyen, Christopher Raub, George Nehmetallah

Proceedings Volume 11396, 113960S (2020) https://doi.org/10.1117/12.2557537

KEYWORDS: Digital holography, Holography, Digital recording, Spatial frequencies, Holograms, Microscopy, Optical simulations, Charge-coupled devices, Digital filtering, Convolution

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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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Proceedings Article | 13 May 2019 Presentation + Paper

Deep learning in computational microscopy

Thanh Nguyen, George Nehmetallah, Lei Tian

Proceedings Volume 10990, 1099007 (2019) https://doi.org/10.1117/12.2520089

KEYWORDS: 3D modeling, Refraction, 3D image processing, Spatial light modulators, Video, Microscopy, Charge-coupled devices, Tomography, Data modeling, Diffraction

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Proceedings Article | 14 May 2018 Presentation + Paper

2D and 3D computational optical imaging using deep convolutional neural networks (DCNNs)

Thanh Nguyen, George Nehmetallah

Proceedings Volume 10667, 1066702 (2018) https://doi.org/10.1117/12.2303995

KEYWORDS: 3D modeling, Spatial light modulators, Refraction, 3D image processing, Charge-coupled devices, Diffraction, Inverse optics, Optical imaging, Convolutional neural networks, Tomography

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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 | 21 February 2017 Presentation + Paper

Quantitative assessment of cancer cell morphology and movement using telecentric digital holographic microscopy

Thanh Nguyen, George Nehmetallah, Van Lam, Byung Min Chung, Christopher Raub

Proceedings Volume 10074, 100740U (2017) https://doi.org/10.1117/12.2256128

KEYWORDS: Digital holography, Cancer, Holography, Microscopy, Live cell imaging, Collagen, Dynamical systems, Microscopes, Molybdenum, Holograms, Breast cancer, Polymers

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Proceedings Article | 19 May 2016 Paper

SLM-based tomography of phase objects using single-shot transport of intensity technique

Georges Nehmetallah, Thanh Nguyen, Dat Tran, Ahmad Darudi, Peyman Soltani

Proceedings Volume 9868, 98680H (2016) https://doi.org/10.1117/12.2220261

KEYWORDS: Spatial light modulators, Tomography, Charge-coupled devices, Diffraction, 3D acquisition, Interferometry, 3D image processing, CCD cameras, Mirrors, Glasses

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Proceedings Article | 19 May 2016 Paper

Detailed analysis of an optimized FPP-based 3D imaging system

Dat Tran, Anh Thai, Kiet Duong, Thanh Nguyen, Georges Nehmetallah

Proceedings Volume 9868, 986802 (2016) https://doi.org/10.1117/12.2220265

KEYWORDS: Imaging systems, 3D metrology, Fringe analysis, Cameras, Calibration, 3D image processing, Projection systems, 3D image reconstruction, Reconstruction algorithms, Distortion

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Proceedings Article | 22 May 2015 Paper

3D high speed characterization of phase objects using the transport of intensity equation

Thanh Nguyen, Georges Nehmetallah, Ahmad Darudi, Peyman Soltani

Proceedings Volume 9495, 949512 (2015) https://doi.org/10.1117/12.2176866

KEYWORDS: Charge-coupled devices, Tomography, CCD cameras, 3D image processing, 3D modeling, Radon transform, Optical filters, Fourier transforms, Diffraction, Spherical lenses

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Showing 5 of 11 publications

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