兰州大学机构库
Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method
Lou, Xiao1,2,3; Zhu, Youzhe4; Punithakumar, Kumaradevan3; Le, Lawrence H.3; Li, Baosheng1,2
2020
Source PublicationIEEE Access   Impact Factor & Quartile Of Published Year  The Latest Impact Factor & Quartile
ISSN2169-3536
Volume8Issue:*Pages:202459-202468
page numbers10
Abstract

Esophagus segmentation in computed tomography images is challenging due to the complex shape and low contrast of the esophagus. Fully automated segmentation is feasible with recent convolutional neural network approaches, such as U-Net, which reduce variability and increase reproducibility. However, these supervised deep learning methods require radiologists to laboriously interpret and label images, which is time-consuming, at the expense of patient care. We propose an esophagus segmentation method using a U-Net neural network combined with several variations of backbones. We also propose a semiautomatic labeling method with detection and execution components to solve the labeling problem. The detection component identifies the category to which each slice belongs using the bag-of-features method. The edges in each category are clustered using contour moments and their topological levels as features. In the execution component, the assumed esophageal contours are predicted by the clustered model. A convex hull approach and level set algorithm yield the final esophageal contours, which are employed to train the neural network. Several backbones are implemented as the encoder of the U-Net network to extract features. The predictions are then compared with those obtained via manual labeling by a radiologist and the segmentation results generated by the proposed semiautomatic method. The experimental evaluations demonstrate that the utilization of ResneXt50 and InceptionV3 as backbones with U-Net is more effective than that with other backbones. A three-dimensional rendering of the segmented model is performed to exhibit the prediction. The results demonstrate that the proposed method outperforms previously published methods.

KeywordComputed Tomography Deep Learning Esophagus Segmentation U-net Automation Computerized tomography Deep learning Image segmentation Learning systems Three dimensional computer graphicsComputed tomography images Convex-hull approach Experimental evaluation Level set algorithm Segmentation methods Segmentation results Semiautomatic methods Topological levels
PublisherInstitute of Electrical and Electronics Engineers Inc.
DOI10.1109/ACCESS.2020.3035772
Indexed BySCIE ; EI
Language英语
Funding ProjectNational Natural Science Foundation of China[81530060][81874224] ; National Key Research and Development Program of China[2016YFC0105106] ; Provincial Key Research and Development Program of Shandong[2017CXZC1206] ; Shandong Academy of Medical Sciences[2019LJ004]
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000589733100001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
EI Accession Number20204709516418
EI KeywordsConvolutional neural networks
EI Classification Number723.5 Computer Applications ; 731 Automatic Control Principles and Applications
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/441532
Collection兰州大学
Corresponding AuthorLi, Baosheng
Affiliation
1.Southeast Univ, Sch Comp Sci & Engn, Lab Image Sci & Technol, Nanjing 210096, Peoples R China
2.Shandong First Med Univ & Shandong Acad Med Sci, Shandong Canc Hosp & Inst, Jinan 250117, Peoples R China
3.Univ Alberta, Dept Radiol & Diagnost Imaging, Edmonton, AB, Canada
4.Lanzhou Univ, Dept Radiat Oncol, First Hosp, Lanzhou 730000, Peoples R China
Recommended Citation
GB/T 7714
Lou, Xiao,Zhu, Youzhe,Punithakumar, Kumaradevan,et al. Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method[J]. IEEE Access,2020,8(*):202459-202468.
APA Lou, Xiao,Zhu, Youzhe,Punithakumar, Kumaradevan,Le, Lawrence H.,&Li, Baosheng.(2020).Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method.IEEE Access,8(*),202459-202468.
MLA Lou, Xiao,et al."Esophagus Segmentation in Computed Tomography Images Using a U-Net Neural Network With a Semiautomatic Labeling Method".IEEE Access 8.*(2020):202459-202468.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Lou, Xiao]'s Articles
[Zhu, Youzhe]'s Articles
[Punithakumar, Kumaradevan]'s Articles
Baidu academic
Similar articles in Baidu academic
[Lou, Xiao]'s Articles
[Zhu, Youzhe]'s Articles
[Punithakumar, Kumaradevan]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Lou, Xiao]'s Articles
[Zhu, Youzhe]'s Articles
[Punithakumar, Kumaradevan]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: 10.1109@ACCESS.2020.3035772.pdf
Format: Adobe PDF
File name: Lou-2020-Esophagus Segmentation in Computed To.pdf
Format: Adobe PDF
No comment.
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.