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

Chlis NK, Karlas A, Fasoula NA, Kallmayer M, Eckstein HH, Theis FJ, Ntziachristos V, Marr C (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Photoacoustics Elsevier

Book Volume: 20

Article Number: 100203

DOI: 10.1016/j.pacs.2020.100203

Abstract

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.

Involved external institutions

Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich DE Germany (DE) Klinikum rechts der Isar DE Germany (DE)

How to cite

APA:

Chlis, N.-K., Karlas, A., Fasoula, N.-A., Kallmayer, M., Eckstein, H.-H., Theis, F.J.,... Marr, C. (2020). A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography. Photoacoustics, 20. https://doi.org/10.1016/j.pacs.2020.100203

MLA:

Chlis, Nikolaos-Kosmas, et al. "A sparse deep learning approach for automatic segmentation of human vasculature in multispectral optoacoustic tomography." Photoacoustics 20 (2020).

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