Open AccessLymphatic vessel segmentation in optical coherence tomography by adding U-Net-based CNN for artifact minimization
Author(s) -
Pei Yu Lai,
Chung Hsing Chang,
Hong Ren Su,
Wen Chuan Kuo
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.389373
Subject(s) - optical coherence tomography , artificial intelligence , thresholding , hessian matrix , computer science , segmentation , ghost imaging , pixel , computer vision , lymphatic system , pattern recognition (psychology) , artifact (error) , optics , physics , mathematics , image (mathematics) , medicine , immunology
The lymphatic system branches throughout the body to transport bodily fluid and plays a key immune-response role. Optical coherence tomography (OCT) is an emerging technique for the noninvasive and label-free imaging of lymphatic capillaries utilizing low scattering features of the lymph fluid. Here, the proposed lymphatic segmentation method combines U-Net-based CNN, a Hessian vesselness filter, and a modified intensity-thresholding to search the nearby pixels based on the binarized Hessian mask. Compared to previous approaches, the method can extract shapes more precisely, and the segmented result contains minimal artifacts, achieves the dice coefficient of 0.83, precision of 0.859, and recall of 0.803.