Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jul 7, 2020; 26(25): 3650-3659
Published online Jul 7, 2020. doi: 10.3748/wjg.v26.i25.3650
Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions
Misaki Kanai, Ren Togo, Takahiro Ogawa, Miki Haseyama
Misaki Kanai, Graduate School of Information Science and Technology, Hokkaido University, Sapporo 0600814, Hokkaido, Japan
Ren Togo, Education and Research Center for Mathematical and Data Science, Hokkaido University, Sapporo 0600812, Hokkaido, Japan
Takahiro Ogawa, Miki Haseyama, Faculty of Information Science and Technology, Hokkaido University, Sapporo 0600814, Hokkaido, Japan
Author contributions: Kanai M and Togo R wrote the paper; Kanai M performed the majority of experiments and analyzed the data; Ogawa T and Haseyama M designed and coordinated the research.
Supported by JSPS KAKENHI Grant, No. JP17H01744.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of The University of Tokyo Hospital.
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous data that were obtained after each patient agreed to inspections by written consent.
Conflict-of-interest statement: The authors have no conflict of interest.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Ren Togo, Education and Research Center for Mathematical and Data Science, Hokkaido University, N-12, W-7, Kita-Ku, Sapporo 0600812, Hokkaido, Japan.
Received: February 10, 2020
Peer-review started: February 10, 2020
First decision: March 15, 2020
Revised: April 3, 2020
Accepted: June 18, 2020
Article in press: June 18, 2020
Published online: July 7, 2020

The risk of gastric cancer increases in patients with Helicobacter pylori-associated chronic atrophic gastritis (CAG). X-ray examination can evaluate the condition of the stomach, and it can be used for gastric cancer mass screening. However, skilled doctors for interpretation of X-ray examination are decreasing due to the diverse of inspections.


To evaluate the effectiveness of stomach regions that are automatically estimated by a deep learning-based model for CAG detection.


We used 815 gastric X-ray images (GXIs) obtained from 815 subjects. The ground truth of this study was the diagnostic results in X-ray and endoscopic examinations. For a part of GXIs for training, the stomach regions are manually annotated. A model for automatic estimation of the stomach regions is trained with the GXIs. For the rest of them, the stomach regions are automatically estimated. Finally, a model for automatic CAG detection is trained with all GXIs for training.


In the case that the stomach regions were manually annotated for only 10 GXIs and 30 GXIs, the harmonic mean of sensitivity and specificity of CAG detection were 0.955 ± 0.002 and 0.963 ± 0.004, respectively.


By estimating stomach regions automatically, our method contributes to the reduction of the workload of manual annotation and the accurate detection of the CAG.

Keywords: Gastric cancer risk, Chronic atrophic gastritis, Helicobacter pylori, Gastric X-ray images, Deep learning, Convolutional neural network, Computer-aided diagnosis

Core tip: To construct a computer-aided diagnosis system, a method to detect chronic atrophic gastritis from gastric X-ray images (GXIs) with a patch-based convolutional neural network is presented in this paper. The proposed method utilizes two GXI groups for training: a manual annotation group and an automatic annotation group. The manual annotation group consists of GXIs for which we manually annotate the stomach regions, and the automatic annotation group consists of GXIs for which we automatically estimate the stomach regions. By utilizing GXIs with the stomach regions for training, the proposed method enables chronic atrophic gastritis detection that automatically eliminates the negative effect of the outside regions.