Early esophagus cancer segmentation from gastrointestinal endoscopic images based on U-Net++model  

作　　者：Zenebe Markos Lonseko Cheng-Si Luo Wen-Ju Du Tao Gan Lin-Lin Zhu Prince Ebenezer Adjei Ni-Ni Rao 

机构地区：[1]School of Life Science and Technology,University of Electronic Science and Technology of China,Chengdu,610054,China [2]College of Health and Medical Sciences,Dilla University,Dilla,419,Ethiopia [3]West China Hospital of Sichuan University,Chengdu,610017,China

出　　处：《Journal of Electronic Science and Technology》2023年第3期38-51,共14页电子科技学刊（英文版）

基　　金：supported by the National Natural Science Foundation under Grants No.62271127,No.61872405,and No.81171411;Natural Science Foundation of Sichuan Province,China under Grant No.23NSFSC0627;Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China and West China Hospital of Sichuan University under Grants No.ZYGX2022YGRH011 and No.HXDZ22005.

摘　　要：Automatic segmentation of early esophagus cancer(EEC)in gastrointestinal endoscopy(GIE)images is a critical and challenging task in clinical settings,which relies primarily on labor-intensive and time-consuming routines.EEC has often been diagnosed at the late stage since early signs of cancer are not obvious,resulting in low survival rates.This work proposes a deep learning approach based on the U-Net++method to segment EEC in GIE images.A total of 2690 GIE images collected from 617 patients at the Digestive Endoscopy Center,West China Hospital of Sichuan University,China,have been utilized.The experimental result shows that our proposed method achieved promising results.Furthermore,the comparison has been made between the proposed and other U-Net-related methods using the same dataset.The mean and standard deviation(SD)of the dice similarity coefficient(DSC),intersection over union(IoU),precision(Pre),and recall(Rec)achieved by the proposed framework were DSC(%)=94.62±0.02,IoU(%)=90.99±0.04,Pre(%)=94.61±0.04,and Rec(%)=95.00±0.02,respectively,outperforming the others.The proposed method has the potential to be applied in EEC automatic diagnoses.

关 键 词：Early esophageal cancer(EEC) Gastrointestinal endoscopic(GIE) images Semantic segmentation Supervised learning U-Net++ 

分 类 号：R563.1[医药卫生—呼吸系统] TP391.41[医药卫生—内科学] TP18[医药卫生—临床医学]