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

Photoacoustics

Hans-Henning Eckstein¹  Vasilis Ntziachristos²  Fabian J. Theis³  Angelos Karlas⁴  Nikolina-Alexia Fasoula⁵  Carsten Marr⁶  Michael Kallmayer⁶  Nikolaos-Kosmas Chlis⁷ 

[1] Chair of Biological Imaging and Center for Translational Cancer Research (TranslaTUM), Munich, Germany;Clinic for Vascular and Endovascular Surgery, Rechts Der Isar Hospital, Munich, Germany;DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany;Institute of Biological and Medical Imaging, Helmholtz Center Munich, Neuherberg, Germany;Roche Pharma Research and Early Development, Large Molecule Research, Roche Innovation Center Munich, Penzberg 82377, Germany;Institute of Biological and Medical Imaging, Helmholtz Center Munich, Neuherberg, Germany;Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany;

关键词: Segmentation;    Deep learning;    Machine learning;    Artificial intelligence;    Clinical;    Translational;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown