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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)
DOI
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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Publikationstyp Artikel: Konferenzbeitrag
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Zeitschrift Proceedings of SPIE
Quellenangaben Band: 13938 Heft: , Seiten: , Artikelnummer: , Supplement: ,
Verlag SPIE
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)