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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
Korrespondenzautor
Schlagwörter Artificial Intelligence ; Clinical ; Deep Learning ; Machine Learning ; Multispectral Optoacoustic Tomography ; Segmentation ; Translational; Selection; Model
ISSN (print) / ISBN 2213-5979
Zeitschrift Photoacoustics
Quellenangaben Band: 20, Heft: , Seiten: , Artikelnummer: 100203 Supplement: ,
Verlag Elsevier
Verlagsort Hackerbrucke 6, 80335 Munich, Germany
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Biological and Medical Imaging (IBMI)
Institute of Computational Biology (ICB)
Förderungen Helmholtz Zentrum Munchen
Helmholtz Association
Deutsches Zentrum für Herz-Kreislaufforschung
European Research Council