Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images.
Ziyun YangSomayyeh Soltanian-ZadehKengyeh K ChuHaoran ZhangLama MoussaAriel E WattsNicholas J ShaheenAdam WaxSina FarsiuPublished in: Biomedical optics express (2021)
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.
Keyphrases
- deep learning
- optical coherence tomography
- neural network
- convolutional neural network
- endothelial cells
- artificial intelligence
- diabetic retinopathy
- resting state
- machine learning
- white matter
- functional connectivity
- induced pluripotent stem cells
- optic nerve
- end stage renal disease
- chronic kidney disease
- gene expression
- ejection fraction
- big data
- newly diagnosed
- peritoneal dialysis
- electronic health record
- patient reported outcomes
- quantum dots
- multiple sclerosis
- energy transfer