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He, H. ; Paetzold, J.C.* ; Börner, N.* ; Riedel, E.* ; Gerl, S. ; Schneider, S.* ; Fisher, C.* ; Ezhov, I.* ; Shit, S.* ; Li, H.* ; Rückert, D.* ; Aguirre, J.* ; Biedermann, T.* ; Darsow, U.* ; Menze, B.* ; Ntziachristos, V.

Automatic quantification of human skin anatomy and microvasculature biomarkers by optoacoustic mesoscopy with machine learning.

In:. SPIE, 2025. DOI: 10.1117/12.3098201 (Proc. SPIE ; 13938)

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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

Publisher SPIE

Reviewing status Peer reviewed

Institute(s) Institute of Biological and Medical Imaging (IBMI)