CASIA OpenIR  > 类脑智能研究中心  > 微观重建与智能分析
Expected affine: A registration method for damaged section in serial sections electron microscopy
Xin, Tong1,2; Shen, Lijun1; Li, Linlin1; Chen, Xi1; Han, Hua1,3,4,5
Source PublicationFRONTIERS IN NEUROINFORMATICS
2022-09-02
Volume16Pages:11
Corresponding AuthorChen, Xi(xi.chen@ia.ac.cn) ; Han, Hua(hua.han@ia.ac.cn)
AbstractRegistration is essential for the volume reconstruction of biological tissues using serial section electron microscope (ssEM) images. However, due to environmental disturbance in section preparation, damage in long serial sections is inevitable. It is difficult to register the damaged sections with the common serial section registration method, creating significant challenges in subsequent neuron tracking and reconstruction. This paper proposes a general registration method that can be used to register damaged sections. This method first extracts the key points and descriptors of the sections to be registered and matches them via a mutual nearest neighbor matcher. K-means and Random Sample Consensus (RANSAC) are used to cluster the key points and approximate the local affine matrices of those clusters. Then, K-nearest neighbor (KNN) is used to estimate the probability density of each cluster and calculate the expected affine matrix for each coordinate point. In clustering and probability density calculations, instead of the Euclidean distance, the path distance is used to measure the correlation between sampling points. The experimental results on real test images show that this method solves the problem of registering damaged sections and contributes to the 3D reconstruction of electronic microscopic images of biological tissues. The code of this paper is available at .
Keywordimage registration SSEM broken sections section fold section crack
DOI10.3389/fninf.2022.944050
WOS KeywordRECONSTRUCTION ; FORMALDEHYDE ; MRI
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences[XDB32030208] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA27010403] ; Bureau of International Cooperation, Chinese Academy of Sciences[153D31KYSB20170059] ; Program of Beijing Municipal Science and Technology Commission[Z201100008420004] ; CAS Key Technology Talent Program[292019000126]
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences ; Bureau of International Cooperation, Chinese Academy of Sciences ; Program of Beijing Municipal Science and Technology Commission ; CAS Key Technology Talent Program
WOS Research AreaMathematical & Computational Biology ; Neurosciences & Neurology
WOS SubjectMathematical & Computational Biology ; Neurosciences
WOS IDWOS:000855279100001
PublisherFRONTIERS MEDIA SA
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/50123
Collection类脑智能研究中心_微观重建与智能分析
Corresponding AuthorChen, Xi; Han, Hua
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Xin, Tong,Shen, Lijun,Li, Linlin,et al. Expected affine: A registration method for damaged section in serial sections electron microscopy[J]. FRONTIERS IN NEUROINFORMATICS,2022,16:11.
APA Xin, Tong,Shen, Lijun,Li, Linlin,Chen, Xi,&Han, Hua.(2022).Expected affine: A registration method for damaged section in serial sections electron microscopy.FRONTIERS IN NEUROINFORMATICS,16,11.
MLA Xin, Tong,et al."Expected affine: A registration method for damaged section in serial sections electron microscopy".FRONTIERS IN NEUROINFORMATICS 16(2022):11.
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