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Diagnosis of Neoplasia in Barrett’s Esophagus using Vital-dye Enhanced Fluorescence Imaging

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Identification of Barrett's esophagus in endoscopic images using deep learning

Wen Pan^1,2, Xujia Li³, Weijia Wang⁴

¹Department of Digestion, West China Hospital of Sichuan University, Chengdu, 610054, Sichuan, China.

BMC Gastroenterology

|December 18, 2021

Abstract

Background:

Development of a deep learning method to identify Barrett's esophagus (BE) scopes in endoscopic images.

Methods:

443 endoscopic images from 187 patients of BE were included in this study. The gastroesophageal junction (GEJ) and squamous-columnar junction (SCJ) of BE were manually annotated in endoscopic images by experts. Fully convolutional neural networks (FCN) were developed to automatically identify the BE scopes in endoscopic images. The networks were trained and evaluated in two separate image sets. The performance of segmentation was evaluated by intersection over union (IOU).

Results:

The deep learning method was proved to be satisfying in the automated identification of BE in endoscopic images. The values of the IOU were 0.56 (GEJ) and 0.82 (SCJ), respectively.

Conclusions:

Deep learning algorithm is promising with accuracies of concordance with manual human assessment in segmentation of the BE scope in endoscopic images. This automated recognition method helps clinicians to locate and recognize the scopes of BE in endoscopic examinations.

Keywords:

Barrett's esophagus Deep learning Esophagoscope Fully convolutional networks Segmentation

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