M-region segmentation of pharyngeal swab image based on improved U-Net Model

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 光电信息技术研究室

	M-region segmentation of pharyngeal swab image based on improved U-Net Model
	Wang, Yina 1; Xu ZC(许泽超)1; Zhao HC(赵怀慈)3; Yang JY(杨俊友)1; Wang, Shuoyu 2
	2021
会议日期	March 4-6, 2021
会议地点	Virtual, Nagoya, Japan
页码	186-190
英文摘要	The main method to diagnose COVID-19 is a nucleic acid test from a throat swab. Routine manual collection methods expose medical personnel to high-risk environment, which has a high risk of cross-infection. A throat swab sampling robot was developed to take the place of medical staff. The automatic segmentation of M-region in the pharyngeal swab image, which plays a core guiding role when the robot takes a throat swab sample. Aiming at the problem of discontinuous or fuzzy boundary in M -region of oral cavity, the segmentation accuracy is affected. An improved U -Net model is proposed and a new multi-scale feature fusion module with channel attention mechanism is presented. The ability of adaptive learning is enhanced and the segmentation precision of M -region with discontinuous or fuzzy edges is increased. Oral images of 45 volunteers were collected for training and testing. Experimental results showed that the model could accurately segment M-region in pharyngeal swab images, and compared with other segmentation networks, it has better indexes of segmentation precision.
源文献作者	IEEE Robotics and Automation Society ; Nagoya University
产权排序	2
会议录	ISR 2021 - 2021 IEEE International Conference on Intelligence and Safety for Robotics
会议录出版者	IEEE
会议录出版地	New York
语种	英语
ISBN号	978-1-6654-3862-9
WOS记录号	WOS:000678996900043
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/28939]
专题	沈阳自动化研究所_光电信息技术研究室
通讯作者	Wang, Yina
作者单位	1.School of Electrical Engineering, Shenyang University of Technology, ShenYang, China 2.School of Systems Engineering, Kochi University of Technology, Kami, Japan 3.Shenyang Institute of Automation, Chinese Academy of Sciences, ShenYang, China
推荐引用方式 GB/T 7714	Wang, Yina,Xu ZC,Zhao HC,et al. M-region segmentation of pharyngeal swab image based on improved U-Net Model[C]. 见:. Virtual, Nagoya, Japan. March 4-6, 2021.