EA-UNet Based Segmentation Method for OCT Image of Uterine Cavity

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成果类型：

期刊论文

作者：

Xiao, Zhang;Du, Meng;Liu, Junjie;Sun, Erjie;Zhang, Jinke;...

通讯作者：

Xiaojing Gong<&wdkj&>Zhiyi Chen

作者机构：

[Xiao, Zhang; Liu, Junjie; Sun, Erjie] Univ South China, Coll Mech Engn, Hengyang 421001, Peoples R China.

[Du, Meng; Xiao, Zhang; Chen, Zhiyi] Univ South China, Inst Med Imaging, Hengyang Med Sch, Hengyang 421001, Peoples R China.

[Du, Meng; Chen, Zhiyi] Univ South China, Affiliated Hosp 1, Med Imaging Ctr, Hengyang Med Sch, Hengyang 421001, Peoples R China.

[Zhang, Jinke; Gong, Xiaojing] Chinese Acad Sci, Shenzhen Inst Adv Technol, Res Ctr Biomed Opt & Mol Imaging, Shenzhen Key Lab Mol Imaging,Guangdong Prov Key La, Shenzhen 518055, Peoples R China.

[Chen, Zhiyi] Univ South China, Affiliated Hosp 7, Hunan Vet Adm Hosp, Hengyang Med Sch, Changsha 410000, Peoples R China.

通讯机构：

[Xiaojing Gong; Zhiyi Chen] A

Authors to whom correspondence should be addressed.<&wdkj&>Institute of Medical Imaging, Hengyang Medical School, University of South China, Hengyang 421001, China<&wdkj&>The First Affiliated Hospital, Medical Imaging Centre, Hengyang Medical School, University of South China, Hengyang 421001, China<&wdkj&>The Seventh Affiliated Hospital, Hunan Veterans Administration Hospital, Hengyang Medical School, University of South China, Changsha 410000, China<&wdkj&>Authors to whom correspondence should be addressed.<&wdkj&>Research Center for Biomedical Optics and Molecular Imaging, Shenzhen Key Laboratory for Molecular Imaging, Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology, CAS Key Laboratory of Health Informatics, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

语种：

英文

关键词：

optical coherence tomography;image segmentation;deep learning;mechanism of attention

期刊：

Photonics

ISSN：

2304-6732

年：

2023

卷：

期：

页码：

73-

DOI：

10.3390/photonics10010073

基金类别：

Conceptualization, Z.X., M.D. and J.L.; methodology, Z.X., M.D. and J.L.; software, Z.X.; validation, Z.X., M.D. and J.L.; formal analysis, M.D., X.G. and Z.C.; investigation, E.S. and J.Z.; resources, X.G.; data curation, Z.C.; writing—original draft preparation, Z.X. and J.L.; writing—review and editing, Z.X. and M.D.; visualization, Z.X.; supervision, J.Z., X.G. and Z.C.; project administration, X.G. and Z.C.; funding acquisition, X.G. and Z.C. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key R&D Program of China (2019YFE0110400), National Natural Science Foundation of China (81971621, 82102087, 82102054), Key R&D Program of Hunan Province (2021SK2035), Natural Science Foundation of Hunan (2022JJ30039, 2022JJ40392), and Clinical Research 4310 Program of the First Affiliated Hospital of The University of South China (4310-2021-K06).

机构署名：

本校为第一机构

院系归属：

机械工程学院

摘要：

Optical coherence tomography (OCT) image processing can provide information about the uterine cavity structure, such as endometrial surface roughness, which is important for the diagnosis of uterine cavity lesions. The accurate segmentation of uterine cavity OCT images is a key step of OCT image processing. We proposed an EA-UNet-based image segmentation model that uses a U-Net network structure with a multi-scale attention mechanism to improve the segmentation accuracy of uterine cavity OCT images. The E(ECA-C) module introduces a convolutiona...

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EA-UNet Based Segmentation Method for OCT Image of Uterine Cavity

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