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Chlis, N.-K. ; Karlas, A. ; Fasoula, N.-A. ; Kallmayer, M.* ; Eckstein, H.H.* ; Theis, F.J. ; Ntziachristos, V. ; Marr, C.

A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography.

Photoacoustics 20:100203 (2020)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Multispectral Optoacoustic Tomography (MSOT) resolves oxy- (HbO2) and deoxy-hemoglobin (Hb) to perform vascular imaging. MSOT suffers from gradual signal attenuation with depth due to light-tissue interactions: an effect that hinders the precise manual segmentation of vessels. Furthermore, vascular assessment requires functional tests, which last several minutes and result in recording thousands of images. Here, we introduce a deep learning approach with a sparse-UNET (S-UNET) for automatic vascular segmentation in MSOT images to avoid the rigorous and time-consuming manual segmentation. We evaluated the S-UNET on a test-set of 33 images, achieving a median DICE score of 0.88. Apart from high segmentation performance, our method based its decision on two wavelengths with physical meaning for the task-at-hand: 850 nm (peak absorption of oxy-hemoglobin) and 810 nm (isosbestic point of oxy-and deoxy-hemoglobin). Thus, our approach achieves precise data-driven vascular segmentation for automated vascular assessment and may boost MSOT further towards its clinical translation.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Artificial Intelligence ; Clinical ; Deep Learning ; Machine Learning ; Multispectral Optoacoustic Tomography ; Segmentation ; Translational; Selection; Model
Sprache englisch
Veröffentlichungsjahr 2020
HGF-Berichtsjahr 2020
ISSN (print) / ISBN 2213-5979
Zeitschrift Photoacoustics
Quellenangaben Band: 20, Heft: , Seiten: , Artikelnummer: 100203 Supplement: ,
Verlag Elsevier
Verlagsort Hackerbrucke 6, 80335 Munich, Germany
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-505500-001
G-503800-001
G-505593-001
G-509200-001
Förderungen Helmholtz Zentrum Munchen
Helmholtz Association
Deutsches Zentrum für Herz-Kreislaufforschung
European Research Council
DOI 10.1016/j.pacs.2020.100203
WOS ID WOS:000606792300004
Scopus ID 85094820424
PubMed ID 33194545
Erfassungsdatum 2020-12-09
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