Invasive and minimally invasive optical detection of pigment accumulation in brain cortex

Luís R. Oliveira
Center of Innovation in Engineering and Industrial Technology, ISEP, Porto, Portugal

Tânia M. Gonçalves
Physics Department, School of Engineering, Polytechnic Institute of Porto, Portugal

Maria R. Pinheiro
Physics Department, School of Engineering, Polytechnic Institute of Porto, Portugal

Luís E. Fernandes
Faculty of Engineering, University of Porto, FEUP, Portugal

Inês S. Martins
Center of Innovation in Engineering and Industrial Technology, ISEP, Porto, Portugal
Faculty of Engineering, University of Porto, FEUP, Portugal

Hugo F. Silva
Center of Innovation in Engineering and Industrial Technology, ISEP, Porto, Portugal

Hélder P. Oliveira
Institute for Systems and Computer Engineering, Technology and Science, INESC TEC, Porto, Portugal
Faculty of Science, University of Porto, FCUP, Portugal

Valery V. Tuchin
Science Medical Center, Saratov State University, Russia
Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, Russia
Laboratory of Laser Diagnostics of Technical and Living Systems, Institute of Precision Mechanics and Control, FRC “Saratov Scientific Centre of the Russian Academy of Sciences”, Saratov, Russia

Luís M. Oliveira orcid (Login required)
Center of Innovation in Engineering and Industrial Technology, ISEP, Porto, Portugal
Physics Department, School of Engineering, Polytechnic Institute of Porto, Portugal

Paper #3467 received 05 Dec 2021; revised manuscript received 07 Mar 2022; accepted for publication 08 Mar 2022; published online 28 Mar 2022.

DOI: 10.18287/JBPE22.08.010304


The estimation of the spectral absorption coefficient of biological tissues provides valuable information that can be used in diagnostic procedures. Such estimation can be made using direct calculations from invasive spectral measurements or though machine learning algorithms based on noninvasive or minimally invasive spectral measurements. Since in a noninvasive approach, the number of measurements is limited, an exploratory study to investigate the use of artificial generated data in machine learning techniques was performed to evaluate the spectral absorption coefficient of the brain cortex. Considering the spectral absorption coefficient that was calculated directly from invasive measurements as reference, the similar spectra that were estimated through different machine learning approaches were able to provide comparable information in terms of pigment, DNA and blood contents in the cortex. The best estimated results were obtained based only on the experimental measurements, but it was also observed that artificially generated spectra can be used in the estimations to increase accuracy, provided that a significant number of experimental spectra are available both to generate the complementary artificial spectra and to estimate the resulting absorption spectrum of the tissue.


tissue spectroscopy; diffuse reflectance; absorption coefficient; brain cortex; DNA content; blood content; pigment detection; machine learning; generative models

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