Dr. Lin-Ching Chang Profile

Dr. Lin-Ching Chang

IRTA Postdoctoral Fellow

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Publications (5)

SPIE Journal Paper | 29 September 2020

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

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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

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

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

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Proceedings Article | 13 March 2010 Paper

Improving RESTORE for robust diffusion tensor estimation: a simulation study

Lin-Ching Chang

Proc. SPIE. 7623, Medical Imaging 2010: Image Processing

KEYWORDS: Diffusion weighted imaging, Data modeling, Magnetic resonance imaging, Diffusion, Interference (communication), Computer simulations, Monte Carlo methods, Anisotropy, Diffusion tensor imaging, Brain

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Proceedings Article | 14 March 2009 Paper

A new linear least squares method for T1 estimation from SPGR signals with multiple TRs

Lin-Ching Chang, Cheng Guan Koay, Peter Basser, Carlo Pierpaoli

Proc. SPIE. 7258, Medical Imaging 2009: Physics of Medical Imaging

KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Scanners, Error analysis, Medical imaging, Transform theory, Data acquisition, Monte Carlo methods, Nonlinear optics, Brain

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Proceedings Article | 29 April 2005 Paper

An automatic method for estimating noise-induced signal variance in magnitude-reconstructed magnetic resonance images

Lin-Ching Chang, Gustavo Rohde, Carlo Pierpaoli

Proc. SPIE. 5747, Medical Imaging 2005: Image Processing

KEYWORDS: Signal to noise ratio, Magnetic resonance imaging, Image segmentation, Error analysis, Diffusion, Magnetism, Interference (communication), Data acquisition, Monte Carlo methods, Data analysis

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