Invasive and minimally invasive optical detection of pigment accumulation in brain cortex
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/22263 |
Resumo: | 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. |
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Invasive and minimally invasive optical detection of pigment accumulation in brain cortexTissue spectroscopyDiffuse reflectanceAbsorption coefficientBrain cortexDNA contentBlood contentPigment detectionMachine learningGenerative modelsThe 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.The authors of the article knew well and communicated with Alexey Bahskatov for many years, especially Valery V. Tuchin and Luís M. Oliveira. We had many joint research discussions, co-authorship in various publications and cooperation in the past. Plans for the future had already been pointed-out, but due to Alexey’s sudden departure, such plans were mercilessly interrupted. We have lost a great scientist and a person with a huge soul, sociable, but at the same time modest and kind. We will always remember our warm meetings and fruitful work with Alexey. This research was supported by the Portuguese grant FCT-UIDB/04730/2020. I.S.M. was supported by the Portuguese grant FCT-UIBD/151528/2021. The work of V.V.T. was supported by the Government of the Russian Federation, Project No. 075-15-2021-615.Samara National Research University, Russian FederationRepositório Científico do Instituto Politécnico do PortoOliveira, LuísGonçalves, TâniaPinheiro, MariaFernandes, LuísMartins, InêsSilva, HugoOliveira, HélderTuchin, ValeryOliveira, Luís2023-02-14T09:46:33Z20222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/22263eng10.18287/JBPE22.08.010304info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:18:53Zoai:recipp.ipp.pt:10400.22/22263Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:42:19.518528Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
title |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
spellingShingle |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex Oliveira, Luís Tissue spectroscopy Diffuse reflectance Absorption coefficient Brain cortex DNA content Blood content Pigment detection Machine learning Generative models |
title_short |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
title_full |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
title_fullStr |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
title_full_unstemmed |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
title_sort |
Invasive and minimally invasive optical detection of pigment accumulation in brain cortex |
author |
Oliveira, Luís |
author_facet |
Oliveira, Luís Gonçalves, Tânia Pinheiro, Maria Fernandes, Luís Martins, Inês Silva, Hugo Oliveira, Hélder Tuchin, Valery |
author_role |
author |
author2 |
Gonçalves, Tânia Pinheiro, Maria Fernandes, Luís Martins, Inês Silva, Hugo Oliveira, Hélder Tuchin, Valery |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Oliveira, Luís Gonçalves, Tânia Pinheiro, Maria Fernandes, Luís Martins, Inês Silva, Hugo Oliveira, Hélder Tuchin, Valery Oliveira, Luís |
dc.subject.por.fl_str_mv |
Tissue spectroscopy Diffuse reflectance Absorption coefficient Brain cortex DNA content Blood content Pigment detection Machine learning Generative models |
topic |
Tissue spectroscopy Diffuse reflectance Absorption coefficient Brain cortex DNA content Blood content Pigment detection Machine learning Generative models |
description |
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. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022-01-01T00:00:00Z 2023-02-14T09:46:33Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.22/22263 |
url |
http://hdl.handle.net/10400.22/22263 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.18287/JBPE22.08.010304 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Samara National Research University, Russian Federation |
publisher.none.fl_str_mv |
Samara National Research University, Russian Federation |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799131510019719168 |