RT Journal Article
SR Electronic
T1 A Sparse Deep Learning Approach for Automatic Segmentation of Human Vasculature in Multispectral Optoacoustic Tomography
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 833251
DO 10.1101/833251
A1 Nikolaos-Kosmas Chlis
A1 Angelos Karlas
A1 Nikolina-Alexia Fasoula
A1 Michael Kallmayer
A1 Hans-Henning Eckstein
A1 Fabian J Theis
A1 Vasilis Ntziachristos
A1 Carsten Marr
YR 2019
UL http://biorxiv.org/content/early/2019/11/07/833251.abstract
AB 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.