Open AccessConnectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images
Author(s) -
Ziyun Yang,
Somayyeh Soltanian-Zadeh,
Kengyeh K. Chu,
Haoran Zhang,
Lama Moussa,
Ariel E. Watts,
Nicholas J. Shaheen,
Adam Wax,
Sina Farsiu
Publication year - 2021
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.434775
Subject(s) - optical coherence tomography , esophagus , artificial intelligence , segmentation , deep learning , computer science , endomicroscopy , in vivo , barrett's esophagus , pixel , image segmentation , pattern recognition (psychology) , computer vision , medicine , radiology , anatomy , biology , adenocarcinoma , confocal , physics , cancer , optics , microbiology and biotechnology
Optical coherence tomography (OCT) is used for diagnosis of esophageal diseases such as Barrett's esophagus. Given the large volume of OCT data acquired, automated analysis is needed. Here we propose a bilateral connectivity-based neural network for in vivo human esophageal OCT layer segmentation. Our method, connectivity-based CE-Net (Bicon-CE), defines layer segmentation as a combination of pixel connectivity modeling and pixel-wise tissue classification. Bicon-CE outperformed other widely used neural networks and reduced common topological prediction issues in tissues from healthy patients and from patients with Barrett's esophagus. This is the first end-to-end learning method developed for automatic segmentation of the epithelium in in vivo human esophageal OCT images.