A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network

doi:10.1093/bioinformatics/btad436

CASIA OpenIR > 脑图谱与类脑智能实验室

	A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network
	Xin, Tong1,2 ; Lv, Yanan1,3 ; Chen, Haoran 1,2; Li, Linlin 3; Shen, Lijun3 ; Shan, Guangcun 4,5; Chen, Xi3 ; Han, Hua2,3,6
发表期刊	BIOINFORMATICS
ISSN	1367-4803
	2023-08-01
卷号	39 期号:8 页码:11
通讯作者	Chen, Xi(xi.chen@ia.ac.cn) ; Han, Hua(hua.han@ia.ac.cn)
摘要	Motivation: The registration of serial section electron microscope images is a critical step in reconstructing biological tissue volumes, and it aims to eliminate complex nonlinear deformations from sectioning and replicate the correct neurite structure. However, due to the inherent properties of biological structures and the challenges posed by section preparation of biological tissues, achieving an accurate registration of serial sections remains a significant challenge. Conventional nonlinear registration techniques, which are effective in eliminating nonlinear deformation, can also eliminate the natural morphological variation of neurites across sections. Additionally, accumulation of registration errors alters the neurite structure.Results:This article proposes a novel method for serial section registration that utilizes an unsupervised optical flow network to measure feature similarity rather than pixel similarity to eliminate nonlinear deformation and achieve pairwise registration between sections. The optical flow network is then employed to estimate and compensate for cumulative registration error, thereby allowing for the reconstruction of the structure of biological tissues. Based on the novel serial section registration method, a serial split technique is proposed for long-serial sections. Experimental results demonstrate that the state-of-the-art method proposed here effectively improves the spatial continuity of serial sections, leading to more accurate registration and improved reconstruction of the structure of biological tissues.
DOI	10.1093/bioinformatics/btad436
关键词[WOS]	SCANNING-ELECTRON-MICROSCOPY
收录类别	SCI
语种	英语
资助项目	STI 2030-Major Projects[2021ZD0204500] ; STI 2030-Major Projects[2021ZD0204503] ; National Natural Science Foundation of China[32171461] ; Instrument Function Development Innovation Program of Chinese Academy of Sciences[E0S92308] ; Instrument Function Development Innovation Program of Chinese Academy of Sciences[E3J1230101] ; Scientific Research Instrument and Equipment Development Project of Chinese Academy of Sciences[YJKYYQ20210022]
项目资助者	STI 2030-Major Projects ; National Natural Science Foundation of China ; Instrument Function Development Innovation Program of Chinese Academy of Sciences ; Scientific Research Instrument and Equipment Development Project of Chinese Academy of Sciences
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:001043321300002
出版者	OXFORD UNIV PRESS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/54024
专题	脑图谱与类脑智能实验室
通讯作者	Chen, Xi; Han, Hua
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 4.Beihang Univ, Sch Instrumentat Sci & Optoelect Engn, Beijing 100083, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data based Precis Med, Beijing 100083, Peoples R China 6.Univ Chinese Acad Sci, Sch Future Technol, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Xin, Tong,Lv, Yanan,Chen, Haoran,et al. A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network[J]. BIOINFORMATICS,2023,39(8):11.
APA	Xin, Tong.,Lv, Yanan.,Chen, Haoran.,Li, Linlin.,Shen, Lijun.,...&Han, Hua.(2023).A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network.BIOINFORMATICS,39(8),11.
MLA	Xin, Tong,et al."A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network".BIOINFORMATICS 39.8(2023):11.