Papers Published

1. Yang, Z; Soltanian-Zadeh, S; Chu, KK; Zhang, H; Moussa, L; Watts, AE; Shaheen, NJ; Wax, A; Farsiu, S, Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images., Biomedical Optics Express, vol. 12 no. 10 (October, 2021), pp. 6326-6340 [doi] .
   (last updated on 2023/06/01)

   Abstract:
   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.