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

doi:10.1016/j.ymeth.2022.07.002

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 生物识别与安全技术研究中心

	Double-branch U-Net for multi-scale organ segmentation
	Liu, Yuhao 5,6; Qin, Caijie 4,6; Yu, Zhiqian 3; Yang, Ruijie 2; Suqing, Tian 2; Liu, Xia 5; Ma, Xibo 1,6
刊名	METHODS
	2022-09-01
卷号	205 页码:220-225
关键词	Multi -scale segmentation Organ segmentation Medical image
ISSN号	1046-2023
DOI	10.1016/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
内容类型	期刊论文
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.