Open AccessHybrid deep learning network for vascular segmentation in photoacoustic imaging
Author(s) -
Alan Yilun Yuan,
Yang Gao,
Liangliang Peng,
Lingxiao Zhou,
Jun Li,
Siwei Zhu,
Wei Song
Publication year - 2020
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.409246
Subject(s) - segmentation , computer science , robustness (evolution) , artificial intelligence , photoacoustic imaging in biomedicine , deep learning , image segmentation , computer vision , vascular network , medical imaging , image processing , optics , image (mathematics) , medicine , anatomy , biochemistry , chemistry , physics , gene
Photoacoustic (PA) technology has been used extensively on vessel imaging due to its capability of identifying molecular specificities and achieving high optical-diffraction-limited lateral resolution down to the cellular level. Vessel images carry essential medical information that provides guidelines for a professional diagnosis. Modern image processing techniques provide a decent contribution to vessel segmentation. However, these methods suffer from under or over-segmentation. Thus, we demonstrate both the results of adopting a fully convolutional network and U-net, and propose a hybrid network consisting of both applied on PA vessel images. Comparison results indicate that the hybrid network can significantly increase the segmentation accuracy and robustness.