Chu Y, Huang F, Gao M, Zou DW, Zhong J, Wu W, Wang Q, Shen XN, Gong TT, Li YY, Wang LF. Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy. World J Gastroenterol 2023; 29(5): 879-889 [PMID: 36816625 DOI: 10.3748/wjg.v29.i5.879]
Corresponding Author of This Article
Li-Fu Wang, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China. lifuwang@sjtu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Feb 7, 2023; 29(5): 879-889 Published online Feb 7, 2023. doi: 10.3748/wjg.v29.i5.879
Convolutional neural network-based segmentation network applied to image recognition of angiodysplasias lesion under capsule endoscopy
Ye Chu, Fang Huang, Min Gao, Duo-Wu Zou, Jie Zhong, Wei Wu, Qi Wang, Xiao-Nan Shen, Ting-Ting Gong, Yuan-Yi Li, Li-Fu Wang
Ye Chu, Duo-Wu Zou, Jie Zhong, Wei Wu, Qi Wang, Xiao-Nan Shen, Ting-Ting Gong, Li-Fu Wang, Department of Gastroenterology, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, Shanghai 200025, China
Fang Huang, Min Gao, Yuan-Yi Li, Technology Platform Department, Jinshan Science & Technology (Group) Co., Ltd., Chongqing 401120, China
Author contributions: Chu Y read and reviewed capsule endoscopy imagines, and participated in the design, writing, and revision of the manuscript; Huang F performed AI algorithm research, designed the segmentation recognition protocol, and verified the algorithm; Gao M organized the experimental results, visualized the experiment, and wrote the draft of the paper; Zou DW designed and revised the protocol of the paper; Zhong J reviewed capsule endoscopy images; Wu W and Wang Q participated in the reading of CE and preparation of the material; Shen XN and Gong TT partial writing of the manuscript; Li YY contributed to the coding and debugging of interface between algorithm and software; Wang LF designed and modified the protocol of the paper.
Supported bythe Chongqing Technological Innovation and Application Development Project, Key Technologies and Applications of Cross Media Analysis and Reasoning, No. cstc2019jscx-zdztzxX0037.
Institutional review board statement: The study was reviewed and approved by the Ruijin Hospital Ethics Committee, Shanghai Jiao Tong University School of Medicine [the certification number was (2017) provisional ethics review No. 138].
Informed consent statement: The informed consent statement was waived by the Ruijin Hospital Ethics Committee.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at lifuwang@sjtu.edu.cn. Participants gave informed consent for data sharing.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Li-Fu Wang, MD, PhD, Chief Physician, Professor, Department of Gastroenterology, Shanghai Jiao Tong University School of Medicine, Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China. lifuwang@sjtu.edu.cn
Received: September 25, 2022 Peer-review started: September 25, 2022 First decision: October 18, 2022 Revised: November 26, 2022 Accepted: January 11, 2023 Article in press: January 11, 2023 Published online: February 7, 2023 Processing time: 134 Days and 3.6 Hours
ARTICLE HIGHLIGHTS
Research background
Small intestinal vascular malformations (angiodysplasias) commonly cause small intestinal bleeding. Therefore, capsule endoscopy has become the primary diagnostic method for angiodysplasias. Nevertheless, manual reading of the entire gastrointestinal tract is a time-consuming heavy workload, which affects the accuracy of diagnosis.
Research motivation
The doctor’s manual reading of the entire gastrointestinal tract is time-consuming, and the heavy workload affects the accuracy of the diagnosis. Also, significant progress has been made in semantic segmentation in the field of deep learning.
Research objectives
This study aimed to assist in the diagnosis and increase the detection rate of angiodysplasias in the small intestine, achieve automatic disease detection, and shorten the capsule endoscopy (CE) reading time.
Research methods
A convolutional neural network semantic segmentation model with feature fusion automatically recognizes the category of vascular dysplasia under CE and draws the lesion contour, thus improving the efficiency and accuracy of identifying small intestinal vascular malformation lesions, was proposed.
Research results
The test set constructed in the study achieved satisfactory results: pixel accuracy was 99%, mean intersection over union was 0.69, negative predictive value was 98.74%, and positive predictive value was 94.27%. The model parameter was 46.38 M, the float calculation was 467.2 G, and the time needed to segment and recognize a picture was 0.6 s.
Research conclusions
Constructing a segmentation network based on deep learning to segment and recognize angiodysplasias lesions is an effective and feasible method for diagnosing angiodysplasias lesions.
Research perspectives
The model detects the small intestinal malformation lesions in the capsule endoscopy image data and draws the lesion area through segmentation.
