Parallel deep neural networks for endoscopic oct image segmentation

Dawei Li, Jimin Wu, Yufan He, Xinwen Yao, Wu Yuan, Defu Chen, Hyeon Cheol Park, Shaoyong Yu, Jerry Ladd Prince, Xingde Li

Research output: Contribution to journalArticle

Abstract

We report parallel-trained deep neural networks for automated endoscopic OCT image segmentation feasible even with a limited training data set. These U-Net-based deep neural networks were trained using a modified dice loss function and manual segmentations of ultrahigh-resolution cross-sectional images collected by an 800 nm OCT endoscopic system. The method was tested on in vivo guinea pig esophagus images. Results showed its robust layer segmentation capability with a boundary error of 1.4 µm insensitive to lay topology disorders. To further illustrate its clinical potential, the method was applied to differentiating in vivo OCT esophagus images from an eosinophilic esophagitis (EOE) model and its control group, and the results clearly demonstrated quantitative changes in the top esophageal layers’ thickness in the EOE model.

Original languageEnglish (US)
Article number#349139
Pages (from-to)1126-1135
Number of pages10
JournalBiomedical Optics Express
Volume10
Issue number3
DOIs
StatePublished - Mar 1 2019

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ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

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