Mr. Tadej Tomanič Profile

Tadej Tomanič

at Univ of Ljubljana

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Publications (8)

SPIE Journal Paper | 16 January 2025 Open Access

Robust estimation of skin physiological parameters from hyperspectral images using Bayesian neural networks

Teo Manojlović, Tadej Tomanič, Ivan Štajduhar, Matija Milanič

JBO, Vol. 30, Issue 01, 016004, (January 2025) https://doi.org/10.1117/12.10.1117/1.JBO.30.1.016004

KEYWORDS: Artificial neural networks, Data modeling, Skin, RGB color model, Education and training, Hyperspectral imaging, Tissues, Nervous system, Performance modeling, In vivo imaging

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SPIE Journal Paper | 22 July 2024 Open Access

Hyperspectral imaging of 4T1 mammary carcinomas grown in dorsal skinfold window chambers: spectral renormalization and fluorescence modeling

Tadej Tomanic, Tim Bozic, Bostjan Markelc, Jost Stergar, Gregor Sersa, Matija Milanic

JBO, Vol. 29, Issue 09, 093504, (July 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.9.093504

KEYWORDS: Tumors, Tissues, Hyperspectral imaging, Tumor growth modeling, Glasses, Skin, Reflectivity, Fluorescence, Image segmentation, Imaging systems

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SignificanceHyperspectral imaging (HSI) of murine tumor models grown in dorsal skinfold window chambers (DSWCs) offers invaluable insight into the tumor microenvironment. However, light loss in a glass coverslip is often overlooked, and particular tissue characteristics are improperly modeled, leading to errors in tissue properties extracted from hyperspectral images.AimWe highlight the significance of spectral renormalization in HSI of DSWC models and demonstrate the benefit of incorporating enhanced green fluorescent protein (EGFP) excitation and emission in the skin tissue model for tumors expressing genes to produce EGFP.ApproachWe employed an HSI system for intravital imaging of mice with 4T1 mammary carcinoma in a DSWC over 14 days. We performed spectral renormalization of hyperspectral images based on the measured reflectance spectra of glass coverslips and utilized an inverse adding–doubling (IAD) algorithm with a two-layer murine skin model, to extract tissue parameters, such as total hemoglobin concentration and tissue oxygenation (StO2). The model was upgraded to consider EGFP fluorescence excitation and emission. Moreover, we conducted additional experiments involving tissue phantoms, human forearm skin imaging, and numerical simulations.ResultsHyperspectral image renormalization and the addition of EGFP fluorescence in the murine skin model reduced the mean absolute percentage errors (MAPEs) of fitted and measured spectra by up to 10% in tissue phantoms, 0.55% to 1.5% in the human forearm experiment and numerical simulations, and up to 0.7% in 4T1 tumors. Similarly, the MAPEs for tissue parameters extracted by IAD were reduced by up to 3% in human forearms and numerical simulations. For some parameters, statistically significant differences (p<0.05) were observed in 4T1 tumors. Ultimately, we have shown that fluorescence emission could be helpful for 4T1 tumor segmentation.ConclusionsThe results contribute to improving intravital monitoring of DWSC models using HSI and pave the way for more accurate and precise quantitative imaging.

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Proceedings Article | 20 June 2024 Presentation + Paper

Fast and accurate skin parameter estimation from hyperspectral images using random Fourier features

Teo Manojlović, Tadej Tomanič, Ivan Štajduhar, Matija Milanič

Proceedings Volume 13006, 130060N (2024) https://doi.org/10.1117/12.3029449

KEYWORDS: Education and training, Artificial neural networks, Data modeling, Skin, Hyperspectral imaging, Machine learning, Monte Carlo methods, Image analysis, Tissues, Performance modeling

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Proceedings Article | 19 June 2024 Presentation

Comparison of tissue parameters extracted by inverse adding-doubling and tissue indices in hyperspectral imaging

Črt Keber, Tadej Tomanič, Jošt Stergar, Tim Božič, Simona Kranjc Brezar, Boštjan Markelc, Gregor Serša, Matija Milanič

Proceedings Volume PC13009, PC130090H (2024) https://doi.org/10.1117/12.3022393

KEYWORDS: Tissues, Hyperspectral imaging, Monte Carlo methods, Computer simulations, Tumors, Skin, Reflectivity, Modeling

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Proceedings Article | 18 June 2024 Presentation + Paper

Non-invasive monitoring of 4T1 murine mammary carcinoma blood vessels after electroporation-based therapy using combined hyperspectral imaging and laser speckle contrast imaging

Tadej Tomanic, Crt Keber, Tim Bozic, Bostjan Markelc, Simona Kranjc Brezar, Gregor Sersa, Jost Stergar, Matija Milanic

Proceedings Volume 13010, 130100Q (2024) https://doi.org/10.1117/12.3017034

KEYWORDS: Tumors, Laser speckle contrast imaging, Hyperspectral imaging, Blood circulation, Tissues, Blood vessels, Reflectivity, Cameras, Speckle

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Proceedings Article | 9 August 2023 Paper

Daily monitoring of CT26 murine tumor model using hyperspectral imaging and optical profilometry

Tadej Tomanic, Jost Stergar, Bostjan Markelc, Tim Bozic, Simona Kranjc Brezar, Gregor Sersa, Matija Milanic

Proceedings Volume 12628, 126280A (2023) https://doi.org/10.1117/12.2670927

KEYWORDS: Tumors, Hyperspectral imaging, Tissues, Tumor growth modeling, Optical sensing, Blood vessels, Oxygenation, Image segmentation, 3D image processing, Visible radiation

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Proceedings Article | 7 December 2021 Paper

GPU-accelerated inverse adding-doubling method for analysis of skin hyperspectral images

Tadej Tomanič, Luka Rogelj, Matija Milanič

Proceedings Volume 11919, 119192F (2021) https://doi.org/10.1117/12.2615058

KEYWORDS: Tissues, Hyperspectral imaging, Skin, Reflectivity, Optical properties, Imaging systems, Inverse scattering problem, Scattering, Rayleigh scattering, Cameras

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SPIE Journal Paper | 6 September 2021 Open Access

Effect of curvature correction on parameters extracted from hyperspectral images

Luka Rogelj, Urban Simončič, Tadej Tomanic, Matija Jezeršek, Urban Pavlovčič, Jošt Stergar, Matija Milanič

JBO, Vol. 26, Issue 09, 096003, (September 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.9.096003

KEYWORDS: Absorption, Hyperspectral imaging, Scattering, Reflectivity, Tissue optics, Tissues, Cameras, Optical properties, Skin, 3D image processing

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