CASIA OpenIR  > 类脑智能研究中心  > 微观重建与智能分析
Performance Analysis in Serial-section Electron Microscopy Image Registration of Neuronal Tissue
Chen BH(陈波昊)1,2; Xin T(辛桐)1,2; Han H(韩华)1,3,4,5; Chen X(陈曦)1
2022-04
Conference NameSPIE Medical Imaging
Volume12032
Pages702-709
Conference Date2022-1
Conference Place美国圣地亚哥
Country美国
Abstract

Serial-section electron microscopy is a widely used technique for neuronal circuit reconstruction. However, the continuity of neuronal structure is destroyed when the tissue block is cut into a series of sections. The neuronal morphology in different sections changes with their locations in the tissue block. These content changes in adjacent sections bring a diffculty to the registration of serial electron microscopy images. As a result, the
accuracy of image registration is strongly influenced by neuronal structure variation and section thickness. 
To evaluate registration performance, we use the spherical deformation model as a simulation of the neuron structure to analyze how registration accuracy is affected by section thickness and neuronal structure size. We mathematically describe the trend that the correlation of neuronal structures in two adjacent sections decreases with section thickness. Furthermore, we demonstrate that registration accuracy is negatively correlated with neuronal structure size and section thickness by analyzing the second-order moment of estimated translation. The experimental results of registration on synthetic data demonstrate that registration accuracy decreases with the neuronal structure size.

KeywordRegistration accuracy Serial section Neuronal structure Spherical deformation model
Indexed ByEI
Funding ProjectStrategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB32030200]
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48582
Collection类脑智能研究中心_微观重建与智能分析
Corresponding AuthorChen X(陈曦)
Affiliation1.中国科学院自动化研究所
2.中国科学院大学人工智能学院
3.中国科学院脑科学与智能技术卓越创新中心
4.中国科学院自动化研究所模式识别国家实验室
5.中国科学院大学未来技术学院
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen BH,Xin T,Han H,et al. Performance Analysis in Serial-section Electron Microscopy Image Registration of Neuronal Tissue[C],2022:702-709.
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