Open AccessAutomatic three-dimensional segmentation of endoscopic airway OCT images
Author(s) -
Qi Li,
Kaibin Zheng,
Xipan Li,
Qianjin Feng,
Zhongping Chen,
Wufan Chen
Publication year - 2019
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.10.000642
Subject(s) - computer science , optical coherence tomography , computer vision , artificial intelligence , segmentation , pipeline (software) , image segmentation , artifact (error) , image processing , pattern recognition (psychology) , image (mathematics) , radiology , medicine , programming language
Automatic delineation and segmentation of airway structures from endoscopic optical coherence tomography (OCT) images improve image analysis efficiency and thus has been of particular interest. Conventional two-dimensional automatic segmentation methods, such as the dynamic programming approach, ensures the edge-continuity in the xz-direction (intra-B-scan), but fails to preserve the surface-continuity when concerning the y-direction (inter-B-scan). To solve this, we present a novel automatic three-dimensional (3D) airway segmentation strategy. Our segmentation scheme includes an artifact-oriented pre-processing pipeline and a modified 3D optimal graph search algorithm incorporating adaptive tissue-curvature adjustment. The proposed algorithm is tested on endoscopic airway OCT image data sets acquired by different swept-source OCT platforms, and on different animal and human models. With our method, the results show continuous surface segmentation performance, which is both robust and accurate.