中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 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

文献类型：期刊论文

作者Xin, Tong1,2; Lv, Yanan2,3; Chen, Haoran1,2; Li, Linlin3; Shen, Lijun3; Shan, Guangcun4,5; Chen, Xi3; Han, Hua1,3,6
刊名BIOINFORMATICS
出版日期2023-08-01
卷号39期号:8页码:11
ISSN号1367-4803
DOI10.1093/bioinformatics/btad436
通讯作者Chen, Xi(xi.chen@ia.ac.cn) ; Han, Hua(hua.han@ia.ac.cn)
英文摘要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.
WOS关键词SCANNING-ELECTRON-MICROSCOPY
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]
WOS研究方向Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
语种英语
出版者OXFORD UNIV PRESS
WOS记录号WOS:001043321300002
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/54023]  
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.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 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
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。