Dr. Eddie M. Gil Profile

Dr. Eddie M. Gil

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Publications (10)

SPIE Journal Paper | 30 June 2023 Open Access

Comparing the segmentation of quantitative phase images of neurons using convolutional neural networks trained on simulated and augmented imagery

Eddie Gil, Zachary Steelman, Anna Sedelnikova, Joel Bixler

NPh, Vol. 10, Issue 03, 035004, (June 2023) https://doi.org/10.1117/12.10.1117/1.NPh.10.3.035004

KEYWORDS: Image segmentation, Education and training, Neurons, Data modeling, Performance modeling, Neurophotonics, Tunable filters, Statistical modeling, Image processing, Convolutional neural networks

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Proceedings Article | 16 March 2023 Presentation + Paper

Deep learning-based neuron segmentation in quantitative phase images trained on procedurally generated data

Eddie Gil, Anna Sedelnikova, Zachary Steelman, Joel Bixler

Proceedings Volume 12389, 1238903 (2023) https://doi.org/10.1117/12.2650212

KEYWORDS: Image segmentation, Education and training, Neurons, Data modeling, Gallium nitride, Statistical modeling, Background noise, Performance modeling, Microscopes, Network architectures

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Proceedings Article | 2 March 2022 Presentation + Paper

Segmenting quantitative phase images of neurons using a deep learning model trained on images generated from a neuronal growth model

Eddie Gil, Zachary Steelman, Anna Sedelnikova, Joel Bixler

Proceedings Volume 11970, 1197007 (2022) https://doi.org/10.1117/12.2608770

KEYWORDS: Image segmentation, Neurons, Data modeling, Phase imaging, Neural networks, Gallium nitride, Network architectures, Data acquisition, Convolution, Machine learning

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Proceedings Article | 5 March 2021 Presentation

Seeing the trees through the forest: training generalizable denoising neural networks using simulated data

Eddie Gil, Zachary Coker, Vsevolod Cheburkanov, Joel Bixler, Vladislav Yakovlev

Proceedings Volume 11622, 1162204 (2021) https://doi.org/10.1117/12.2582380

KEYWORDS: Neural networks, Denoising, Signal processing, Signal detection, Raman spectroscopy, Inverse problems, Inverse optics, Interference (communication), Image segmentation, Image processing

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Proceedings Article | 3 March 2020 Paper

Hyper-Raman optical activity of biologically relevant chiral molecules

Christopher Marble, Xingqi Xu, Mark Keppler, Eddie Gil, Georgi Petrov, Dawei Wang, Vladislav Yakovlev

Proceedings Volume 11288, 1128829 (2020) https://doi.org/10.1117/12.2545530

KEYWORDS: Molecules, Optical activity, Polarization, Raman scattering, Scattering, Thermal effects, Raman spectroscopy, Light scattering, Remote sensing, Ultraviolet radiation

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Proceedings Article | 20 February 2020 Presentation + Paper

Neural network generation for estimation of tissue optical properties

Eddie Gil, Brett Hokr, Joel Bixler

Proceedings Volume 11238, 1123809 (2020) https://doi.org/10.1117/12.2546068

KEYWORDS: Monte Carlo methods, Optical properties, Neural networks, Backscatter, Convolution, Tissue optics, Photon transport, Scattering, Tissues, Matrices, Machine learning

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Proceedings Article | 20 February 2020 Paper

Compressed hyperspectral Raman microscope for imaging tissues and cellular structures

Mark Keppler, Eddie Gil, Sean O'Connor, Gary Noojin, Vladislav Yakovlev, Joel Bixler

Proceedings Volume 11238, 112380N (2020) https://doi.org/10.1117/12.2544571

KEYWORDS: Raman spectroscopy, Digital micromirror devices, Microscopes, Spectroscopy, Defense and security, Hyperspectral imaging, Diffraction gratings, Diffraction

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Proceedings Article | 19 February 2020 Paper

Kernel principle component analysis applied to Raman spectra to differentiate drugs administered to rabbit cornea in blind study

Sean O'Connor, Eddie Gil, Mark Keppler, Marlan Scully, Vladislav Yakovlev

Proceedings Volume 11219, 1121907 (2020) https://doi.org/10.1117/12.2546431

KEYWORDS: Cornea, Raman spectroscopy, Microscopes, Luminescence, Principal component analysis, Visualization, Aluminum, Confocal microscopy, Tissue optics, Chemical analysis

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Proceedings Article | 1 March 2019 Presentation + Paper

Comparison of various neural network-based models for retinal lesion analysis

Eddie Gil, Mark Keppler, Vladislav Yakovlev, Adam Boretsky, Joel Bixler

Proceedings Volume 10876, 108760V (2019) https://doi.org/10.1117/12.2510006

KEYWORDS: Image filtering, Image segmentation, Network architectures, Convolution, Linear filtering, Optical filters, Macula, Neural networks, Performance modeling, Convolutional neural networks

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Proceedings Article | 1 March 2019 Paper

Comparison of various neural network-based models for retinal lesion analysis

Eddie Gil, Mark Keppler, Vladislav Yakovlev, Adam Boretsky, Joel Bixler

Proceedings Volume 10876, 108760M (2019) https://doi.org/10.1117/12.2507908

KEYWORDS: Image filtering, Image segmentation, Network architectures, Linear filtering, Convolution, Convolutional neural networks, Macula

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Showing 5 of 10 publications

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