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Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues.

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Τίτλος: Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues.
Συγγραφείς: Harel M; Department of Physics, Ariel University, Ariel, Israel., Chacko N; Department of Physics, Ariel University, Ariel, Israel., Motiei M; Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel., Azaria A; Department of Computer Science, Ariel University, Ariel, Israel., Ankri R; Department of Physics, Ariel University, Ariel, Israel.; Department of Applied Physics, Jerusalem College of Technology, Jerusalem, Israel.
Πηγή: Journal of biophotonics [J Biophotonics] 2026 Jul; Vol. 19 (7), pp. e70325.
Τύπος έκδοσης: Journal Article; Research Support, Non-U.S. Gov't
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Wiley-VCH Country of Publication: Germany NLM ID: 101318567 Publication Model: Print Cited Medium: Internet ISSN: 1864-0648 (Electronic) Linking ISSN: 1864063X NLM ISO Abbreviation: J Biophotonics Subsets: MEDLINE
Imprint Name(s): Original Publication: Weinheim : Wiley-VCH
Ιατρικοί όροι (MeSH): Image Processing, Computer-Assisted*/methods , Optical Imaging* , Scattering, Radiation* , Neural Networks, Computer*, Gold/chemistry ; Skin/diagnostic imaging ; Monte Carlo Method ; Humans ; Phantoms, Imaging
Περίληψη: Fluorescence imaging (FI) is widely used in in vivo and cellular studies. However, accurate depth determination of fluorescent targets in vivo remains challenging due to strong photon scattering and absorption in biological tissues. In this study, we developed and validated a machine learning (ML)-based framework, supported by Monte Carlo (MC) simulations, for accurate depth estimation of near-infrared (NIR) fluorescent targets in turbid media. An optimized MC model was constructed to simulate NIR fluorescence photon propagation, generating datasets of fluorescence images corresponding to target depths between 0.1 and 1 cm. Experimental validation was conducted over a depth range of 0.1-0.7 cm using tissue-mimicking phantoms designed to replicate the optical properties of human skin, as well as on an in vivo model incorporating fluorescent gold nanostructures imaged with computed tomography (CT) and wide-field FI. The results were quantitatively validated using two performance metrics: rounded accuracy and root mean squared error (RMSE).
(© 2026 The Author(s). Journal of Biophotonics published by Wiley‐VCH GmbH.)
References: J Biomed Opt. 2024 Jan;29(1):010901. (PMID: 38269083)
Pharmaceutics. 2022 Nov 17;14(11):. (PMID: 36432688)
Appl Opt. 2005 Apr 1;44(10):1934-41. (PMID: 15813529)
Discov Nano. 2024 Jan 25;19(1):18. (PMID: 38270794)
Biomed Opt Express. 2020 Jun 19;11(7):3857-3874. (PMID: 33014571)
Opt Lett. 2020 Aug 1;45(15):4232-4235. (PMID: 32735266)
Nat Methods. 2019 Dec;16(12):1323-1331. (PMID: 31686039)
IEEE Trans Med Imaging. 2021 Nov;40(11):3217-3228. (PMID: 33826514)
Opt Express. 2007 Aug 6;15(16):10135-48. (PMID: 19547362)
Front Chem. 2021 Oct 18;9:750404. (PMID: 34733821)
Proc Natl Acad Sci U S A. 2007 Dec 4;104(49):19494-9. (PMID: 18042710)
J Biomed Opt. 2024 Feb;29(2):026004. (PMID: 38425720)
Nat Commun. 2022 Mar 11;13(1):1269. (PMID: 35277496)
Chem Soc Rev. 2022 Oct 31;51(21):8957-9008. (PMID: 36226744)
Proc Natl Acad Sci U S A. 2018 Apr 24;115(17):4465-4470. (PMID: 29626132)
J Biophotonics. 2023 Jun;16(6):e202300045. (PMID: 36883623)
J Biomed Opt. 2022 Feb;27(2):. (PMID: 35218169)
Anal Bioanal Chem. 2011 Jul;400(9):2687-97. (PMID: 21455652)
J Biomed Opt. 2020 May;25(7):1-43. (PMID: 32406215)
J Cell Sci. 2021 May 1;134(9):1-17. (PMID: 33961054)
J Biomed Opt. 2013 Oct;18(10):100501. (PMID: 24091624)
Bioinformatics. 2001 Dec;17(12):1213-23. (PMID: 11751230)
Biomed Opt Express. 2019 May 21;10(6):2909-2917. (PMID: 31259061)
Nat Commun. 2019 Apr 30;10(1):1996. (PMID: 31040287)
Digit Health. 2023 Nov 30;9:20552076231216681. (PMID: 38047163)
Grant Information: Planning and Budgeting Committee of the Council for Higher Education of Israel; 704/24 Israel Science Foundation
Contributed Indexing: Keywords: Monte Carlo simulation; fluorescence imaging; machine learning; multiplexed imaging; near‐infrared (NIR)
Substance Nomenclature: 7440-57-5 (Gold)
Entry Date(s): Date Created: 20260726 Date Completed: 20260726 Latest Revision: 20260813
Update Code: 20260814
PubMed Central ID: PMC13401773
DOI: 10.1002/jbio.70325
PMID: 42502922
Βάση Δεδομένων: MEDLINE
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  Data: Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues.
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  Data: <searchLink fieldCode="AU" term="%22Harel+M%22">Harel M</searchLink>; Department of Physics, Ariel University, Ariel, Israel.<br /><searchLink fieldCode="AU" term="%22Chacko+N%22">Chacko N</searchLink>; Department of Physics, Ariel University, Ariel, Israel.<br /><searchLink fieldCode="AU" term="%22Motiei+M%22">Motiei M</searchLink>; Faculty of Engineering and the Institute of Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat Gan, Israel.<br /><searchLink fieldCode="AU" term="%22Azaria+A%22">Azaria A</searchLink>; Department of Computer Science, Ariel University, Ariel, Israel.<br /><searchLink fieldCode="AU" term="%22Ankri+R%22">Ankri R</searchLink>; Department of Physics, Ariel University, Ariel, Israel.; Department of Applied Physics, Jerusalem College of Technology, Jerusalem, Israel.
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  Data: <searchLink fieldCode="JN" term="%22101318567%22">Journal of biophotonics</searchLink> [J Biophotonics] 2026 Jul; Vol. 19 (7), pp. e70325.
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  Data: <i>Original Publication</i>: Weinheim : Wiley-VCH
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  Data: Fluorescence imaging (FI) is widely used in in vivo and cellular studies. However, accurate depth determination of fluorescent targets in vivo remains challenging due to strong photon scattering and absorption in biological tissues. In this study, we developed and validated a machine learning (ML)-based framework, supported by Monte Carlo (MC) simulations, for accurate depth estimation of near-infrared (NIR) fluorescent targets in turbid media. An optimized MC model was constructed to simulate NIR fluorescence photon propagation, generating datasets of fluorescence images corresponding to target depths between 0.1 and 1 cm. Experimental validation was conducted over a depth range of 0.1-0.7 cm using tissue-mimicking phantoms designed to replicate the optical properties of human skin, as well as on an in vivo model incorporating fluorescent gold nanostructures imaged with computed tomography (CT) and wide-field FI. The results were quantitatively validated using two performance metrics: rounded accuracy and root mean squared error (RMSE).<br /> (© 2026 The Author(s). Journal of Biophotonics published by Wiley‐VCH GmbH.)
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  Data: J Biomed Opt. 2024 Jan;29(1):010901. (PMID: <searchLink fieldCode="PM" term="%2238269083%22">38269083)</searchLink><br />Pharmaceutics. 2022 Nov 17;14(11):. (PMID: <searchLink fieldCode="PM" term="%2236432688%22">36432688)</searchLink><br />Appl Opt. 2005 Apr 1;44(10):1934-41. (PMID: <searchLink fieldCode="PM" term="%2215813529%22">15813529)</searchLink><br />Discov Nano. 2024 Jan 25;19(1):18. (PMID: <searchLink fieldCode="PM" term="%2238270794%22">38270794)</searchLink><br />Biomed Opt Express. 2020 Jun 19;11(7):3857-3874. (PMID: <searchLink fieldCode="PM" term="%2233014571%22">33014571)</searchLink><br />Opt Lett. 2020 Aug 1;45(15):4232-4235. (PMID: <searchLink fieldCode="PM" term="%2232735266%22">32735266)</searchLink><br />Nat Methods. 2019 Dec;16(12):1323-1331. (PMID: <searchLink fieldCode="PM" term="%2231686039%22">31686039)</searchLink><br />IEEE Trans Med Imaging. 2021 Nov;40(11):3217-3228. (PMID: <searchLink fieldCode="PM" term="%2233826514%22">33826514)</searchLink><br />Opt Express. 2007 Aug 6;15(16):10135-48. (PMID: <searchLink fieldCode="PM" term="%2219547362%22">19547362)</searchLink><br />Front Chem. 2021 Oct 18;9:750404. (PMID: <searchLink fieldCode="PM" term="%2234733821%22">34733821)</searchLink><br />Proc Natl Acad Sci U S A. 2007 Dec 4;104(49):19494-9. (PMID: <searchLink fieldCode="PM" term="%2218042710%22">18042710)</searchLink><br />J Biomed Opt. 2024 Feb;29(2):026004. (PMID: <searchLink fieldCode="PM" term="%2238425720%22">38425720)</searchLink><br />Nat Commun. 2022 Mar 11;13(1):1269. (PMID: <searchLink fieldCode="PM" term="%2235277496%22">35277496)</searchLink><br />Chem Soc Rev. 2022 Oct 31;51(21):8957-9008. (PMID: <searchLink fieldCode="PM" term="%2236226744%22">36226744)</searchLink><br />Proc Natl Acad Sci U S A. 2018 Apr 24;115(17):4465-4470. (PMID: <searchLink fieldCode="PM" term="%2229626132%22">29626132)</searchLink><br />J Biophotonics. 2023 Jun;16(6):e202300045. (PMID: <searchLink fieldCode="PM" term="%2236883623%22">36883623)</searchLink><br />J Biomed Opt. 2022 Feb;27(2):. (PMID: <searchLink fieldCode="PM" term="%2235218169%22">35218169)</searchLink><br />Anal Bioanal Chem. 2011 Jul;400(9):2687-97. (PMID: <searchLink fieldCode="PM" term="%2221455652%22">21455652)</searchLink><br />J Biomed Opt. 2020 May;25(7):1-43. (PMID: <searchLink fieldCode="PM" term="%2232406215%22">32406215)</searchLink><br />J Cell Sci. 2021 May 1;134(9):1-17. (PMID: <searchLink fieldCode="PM" term="%2233961054%22">33961054)</searchLink><br />J Biomed Opt. 2013 Oct;18(10):100501. (PMID: <searchLink fieldCode="PM" term="%2224091624%22">24091624)</searchLink><br />Bioinformatics. 2001 Dec;17(12):1213-23. (PMID: <searchLink fieldCode="PM" term="%2211751230%22">11751230)</searchLink><br />Biomed Opt Express. 2019 May 21;10(6):2909-2917. (PMID: <searchLink fieldCode="PM" term="%2231259061%22">31259061)</searchLink><br />Nat Commun. 2019 Apr 30;10(1):1996. (PMID: <searchLink fieldCode="PM" term="%2231040287%22">31040287)</searchLink><br />Digit Health. 2023 Nov 30;9:20552076231216681. (PMID: <searchLink fieldCode="PM" term="%2238047163%22">38047163)</searchLink>
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  Data: <i>Keywords: </i>Monte Carlo simulation; fluorescence imaging; machine learning; multiplexed imaging; near‐infrared (NIR)
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