Instance segmentation of biological images using graph convolutional network

doi:10.1016/j.engappai.2022.104739

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 > 三维可视计算

	Instance segmentation of biological images using graph convolutional network
	Xu, Rongtao 1,4; Li, Ye 2; Wang, Changwei 1,4; Xu, Shibiao 3; Meng, Weiliang 4; Zhang, Xiaopeng 4
刊名	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
	2022-04-01
卷号	110 页码:10
关键词	Instance segmentation Biological images GCN Spatial attention Graph-guided feature fusion Instance segmentation Biological images GCN Spatial attention Graph-guided feature fusion
ISSN号	0952-1976
DOI	10.1016/j.engappai.2022.104739
通讯作者	Xu, Shibiao(shibiaoxu@bupt.edu.cn) ; Meng, Weiliang(weiliang.meng@ia.ac.cn)
英文摘要	Instance segmentation in biological images is an important task in the field of biological images and biomedical analysis. Different from the instance segmentation of natural image scenes, this task is still challenging because there are a large number of overlapping objects with similar appearance as well as great variability in shape, size and texture in the foreground and background. In this paper, we propose a novel method for segmentation of graph-guided instances of biological images, which successfully addresses these peculiarities. Our method predicts the embedding at each pixel and uses clustering to recover instances during testing. Specifically, we design the Graph-guided Feature Fusion Module in response to overlapping instances. Our Graph-guided Feature Fusion Module combines fine deep features and coarse shallow features to learn the affinity matrix, and then uses graph convolutional network to guide the network to learn object-level local features. Next, we devise the Gated Spatial Attention Module to effectively learn key spatial information by introducing a gating mechanism. Furthermore, we give the Cluster Distance Loss that can effectively distinguish foreground objects from similar backgrounds. The effectiveness of our proposed method has been verified on various biological and biomedical datasets. The experimental results show that our method is superior to previous embedding-based instance segmentation methods. The SBD metric for our method reached 90.8% on the plant phenotype dataset (CVPPP), 72.5% on the cell nucleus dataset (DSB2018), and 81.8% on the C.elegans dataset, all achieving state-of-the-art performance.
资助项目	National Natural Science Foundation of China[U21A20515] ; National Natural Science Foundation of China[61971418] ; National Natural Science Foundation of China[U2003109] ; National Natural Science Foundation of China[62171321] ; National Natural Science Foundation of China[62071157] ; National Natural Science Foundation of China[62162044] ; Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences[LSU-KFJJ-2020-04]
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
语种	英语
出版者	PERGAMON-ELSEVIER SCIENCE LTD
WOS记录号	WOS:000795645300006
资助机构	National Natural Science Foundation of China ; Open Research Fund of Key Laboratory of Space Utilization, Chinese Academy of Sciences
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49488]
专题	模式识别国家重点实验室_三维可视计算
通讯作者	Xu, Shibiao; Meng, Weiliang
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Technol & Engn Ctr Space Utilizat, Key Lab Space Utilizat, Beijing, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Artificial Intelligence, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Xu, Rongtao,Li, Ye,Wang, Changwei,et al. Instance segmentation of biological images using graph convolutional network[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2022,110:10.
APA	Xu, Rongtao,Li, Ye,Wang, Changwei,Xu, Shibiao,Meng, Weiliang,&Zhang, Xiaopeng.(2022).Instance segmentation of biological images using graph convolutional network.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,110,10.
MLA	Xu, Rongtao,et al."Instance segmentation of biological images using graph convolutional network".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 110(2022):10.