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6 September 2024 Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering
Thomas T. Livecchi, Steven L. Jacques, Hrebesh M. Subhash, Mark C. Pierce
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Journal of Biomedical Optics, Vol. 29, Issue 9, 093507 (September 2024). https://doi.org/10.1117/1.JBO.29.9.093507
Abstract

Significance

Hyperspectral cameras capture spectral information at each pixel in an image. Acquired spectra can be analyzed to estimate quantities of absorbing and scattering components, but the use of traditional fitting algorithms over megapixel images can be computationally intensive. Deep learning algorithms can be trained to rapidly analyze spectral data and can potentially process hyperspectral camera data in real time.

Aim

A hyperspectral camera was used to capture 1216×1936 pixel wide-field reflectance images of in vivo human tissue at 205 wavelength bands from 420 to 830 nm.

Approach

The optical properties of oxyhemoglobin, deoxyhemoglobin, melanin, and scattering were used with multi-layer Monte Carlo models to generate simulated diffuse reflectance spectra for 24,000 random combinations of physiologically relevant tissue components. These spectra were then used to train an artificial neural network (ANN) to predict tissue component concentrations from an input reflectance spectrum.

Results

The ANN achieved low root mean square errors in a test set of 6000 independent simulated diffuse reflectance spectra while calculating concentration values more than 4000× faster than a conventional iterative least squares approach.

Conclusions

In vivo finger occlusion and gingival abrasion studies demonstrate the ability of this approach to rapidly generate high-resolution images of tissue component concentrations from a hyperspectral dataset acquired from human subjects.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Thomas T. Livecchi, Steven L. Jacques, Hrebesh M. Subhash, and Mark C. Pierce "Hyperspectral imaging with deep learning for quantification of tissue hemoglobin, melanin, and scattering," Journal of Biomedical Optics 29(9), 093507 (6 September 2024). https://doi.org/10.1117/1.JBO.29.9.093507
Received: 4 April 2024; Accepted: 20 August 2024; Published: 6 September 2024
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KEYWORDS
Artificial neural networks
Tissues
Scattering
Hyperspectral imaging
Blood
Oxygen
Diffuse reflectance spectroscopy
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