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Double-branch U-Net for multi-scale organ segmentation

文献类型：期刊论文

作者Liu, Yuhao5,6; Qin, Caijie4,6; Yu, Zhiqian3; Yang, Ruijie2; Suqing, Tian2; Liu, Xia5; Ma, Xibo1,6
刊名METHODS
出版日期2022-09-01
卷号205页码:220-225
ISSN号1046-2023
关键词Multi -scale segmentation Organ segmentation Medical image
DOI10.1016/j.ymeth.2022.07.002
通讯作者Liu, Xia(liuxia@hrbust.edu.cn) ; Ma, Xibo(xibo.ma@nlpr.ia.ac.cn)
英文摘要U-Net has achieved great success in the task of medical image segmentation. It encodes and extracts information from several convolution blocks, and then decodes the feature maps to get the segmentation results. Our ex-periments show that in a multi-scale medical segmentation task, excessive downsampling will cause the model to ignore the small segmentation objects and thus fail to complete the segmentation task. In this work, we propose a more complete method Double-branch U-Net (2BUNet) to solve the multi-scale organ segmentation challenge. Our model is divided into four parts: main branch, tributary branch, information exchange module and classi-fication module. The main advantages of the new model consist of: (1) Extracting information to improve model decoding capabilities using the complete encoding structure. (2) The information exchange module is added to the main branch and tributaries to provide regularization for the model, so as to avoid the large gap between the two paths. (3) Main branch structure for extracting major features of large organ. (4) The tributary structure is used to enlarge the image to extract the microscopic characteristics of small organ. (5) A classification assistant module is proposed to increase the class constraint for the output tensor. The comparative experiments show that our method achieves state-of-the-art performances in real scenes.
资助项目National Key Research programs of China[2016YFA0100900] ; National Key Research programs of China[2016YFA0100902] ; Chinese National Natural Science Foundation[82090051] ; Chinese National Natural Science Foundation[81871442] ; Youth Innovation Promotion Association CAS[Y201930]
WOS研究方向Biochemistry & Molecular Biology
语种英语
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
WOS记录号WOS:000835183700002
资助机构National Key Research programs of China ; Chinese National Natural Science Foundation ; Youth Innovation Promotion Association CAS
源URL[http://ir.ia.ac.cn/handle/173211/49800]  
专题自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心
通讯作者Liu, Xia; Ma, Xibo
作者单位1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
2.Peking Univ Third Hosp, Dept Radiat Oncol, Beijing 100190, Peoples R China
3.Northwest Univ, Informat Sci & Technol, Xian, Peoples R China
4.Sanming Univ, Inst Informat Engn, Sanming 365004, Peoples R China
5.Harbin Univ Sci & Technol, Harbin 150080, Peoples R China
6.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Yuhao,Qin, Caijie,Yu, Zhiqian,et al. Double-branch U-Net for multi-scale organ segmentation[J]. METHODS,2022,205:220-225.
APA Liu, Yuhao.,Qin, Caijie.,Yu, Zhiqian.,Yang, Ruijie.,Suqing, Tian.,...&Ma, Xibo.(2022).Double-branch U-Net for multi-scale organ segmentation.METHODS,205,220-225.
MLA Liu, Yuhao,et al."Double-branch U-Net for multi-scale organ segmentation".METHODS 205(2022):220-225.

入库方式： OAI收割

来源：自动化研究所

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