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Open Access Green as soon as Postprint is submitted to ZB.
Automatic quantification of human skin anatomy and microvasculature biomarkers by optoacoustic mesoscopy with machine learning.
In:. 1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa: SPIE, 2025. 3 (Proc. SPIE ; 13938)
We developed a deep learning-based framework to analyze and quantify morphological skin features recorded by raster-scan optoacoustic mesoscopy (RSOM) and extract imaging biomarkers for disease characterization. The automatic biomarker extraction by our method was found to be strongly correlated to physician observations and histology. We investigated for the first time the relation of a number of microvasculature features to aging from 75 healthy volunteers and found that fine microvasculature in the upper dermal layer offers the strongest relation to age progression.
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Publication type
Article: Conference contribution
ISSN (print) / ISBN
0277-786X
e-ISSN
1996-756X
Journal
Proceedings of SPIE
Quellenangaben
Volume: 13938,
Pages: 3
Publisher
SPIE
Publishing Place
1000 20th St, Po Box 10, Bellingham, Wa 98227-0010 Usa
Reviewing status
Peer reviewed
Institute(s)
Institute of Biological and Medical Imaging (IBMI)
Grants
Free State of Bavaria
Helmholtz Initiative and Networking Fund
European Research Council (ERC)
European Union
Helmholtz Initiative and Networking Fund
European Research Council (ERC)
European Union