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He, H. ; Paetzold, J.C.* ; Borner, 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.

Machine learning analysis of human skin by optoacoustic mesoscopy for automated extraction of psoriasis and aging biomarkers.

IEEE Trans. Med. Imaging 43, 2074-2085 (2024)
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Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Biomarkers ; Feature Extraction ; Image Reconstruction ; Image Segmentation ; Imaging ; Machine Learning ; Morphology ; Optoacoustic Mesoscopy ; Photoacoustic ; Segmentation ; Skin ; Skin Aging ; Skin Imaging; Optical Coherence Tomography; Diagnosis
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 0278-0062
e-ISSN 1558-254X
Zeitschrift IEEE Transactions on Medical Imaging
Quellenangaben Band: 43, Heft: 6, Seiten: 2074-2085 Artikelnummer: , Supplement: ,
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Verlagsort New York, NY [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
Förderungen European Union's Horizon 2020 research and innovation programme
DOI 10.1109/TMI.2024.3356180
WOS ID 001243728100009
Scopus ID 85182937366
PubMed ID 38241120
Erfassungsdatum 2024-01-29
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