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Expected affine: A registration method for damaged section in serial sections electron microscopy | |
Xin, Tong1,2![]() ![]() ![]() ![]() ![]() | |
发表期刊 | FRONTIERS IN NEUROINFORMATICS
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2022-09-02 | |
卷号 | 16页码:11 |
摘要 | Registration 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 . |
关键词 | image registration SSEM broken sections section fold section crack |
DOI | 10.3389/fninf.2022.944050 |
关键词[WOS] | RECONSTRUCTION ; FORMALDEHYDE ; MRI |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Strategic 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] |
项目资助者 | Strategic 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研究方向 | Mathematical & Computational Biology ; Neurosciences & Neurology |
WOS类目 | Mathematical & Computational Biology ; Neurosciences |
WOS记录号 | WOS:000855279100001 |
出版者 | FRONTIERS MEDIA SA |
七大方向——子方向分类 | 医学影像处理与分析 |
国重实验室规划方向分类 | AI For Science |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50123 |
专题 | 脑图谱与类脑智能实验室_微观重建与智能分析 |
通讯作者 | Chen, Xi; Han, Hua |
作者单位 | 1.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 |
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; 模式识别国家重点实验室 |
推荐引用方式 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. |
条目包含的文件 | 条目无相关文件。 |
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