中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Skeleton-based image registration of serial electron microscopy sections

文献类型:会议论文

作者Chen, Xi1; Shen, Lijun1; Xie, Qiwei1; Han, Hua1,2,3,4
出版日期2019
会议日期16 - 21 February 2019
会议地点San Diego, California, United States
英文摘要

Imaging serial sections in electron microcopy (EM) is an important volume EM approach for neuronal circuit reconstruction, which has advantages of larger imaging volume and non-destructive for tissue sections. However, the continuity between sections is destroyed when the tissue block is cut into sections physically, and sections suffer stretching, folding and distorting individually during section preparation and imaging. As a result, image registration is a challenging task to recover the continuity of the neurite. The traditional methods use the SIFT or block matching method to extract landmarks between the adjacent sections, which is doubtful when the neurite direction is not perpendicular to the section plane.

To get round the difficulty of reliable landmark extraction, we propose a skeleton-based image registration method for serial EM sections of the nerve tissue. The virtual skeletons are traced across the sections after an initial approximate rigid alignment. Then we make assumption that the skeleton shape is smooth adequately in z direction. In company with the constraints that the displacements of the skeleton points in the same section are smooth and small, an energy function is proposed to calculate the new positions of the skeleton points for all of the sections. Finally, the sections are warped according to the adjusted positions of skeleton points. The proposed method is highly automatic and could recover the 3D continuity of the neurite. We demonstrate that our method outperforms the state-of-the-art methods on serial EM sections including a synthetic test case.

语种英语
源URL[http://ir.ia.ac.cn/handle/173211/26119]  
专题类脑智能研究中心_微观重建与智能分析
通讯作者Han, Hua
作者单位1.Institute of Automation, CAS
2.School of Future Technology, University of Chinese Academy of Sciences
3.The National Laboratory of Pattern Recognition
4.CAS Center for Excellence in Brain Science and Intelligence Technology
推荐引用方式
GB/T 7714
Chen, Xi,Shen, Lijun,Xie, Qiwei,et al. Skeleton-based image registration of serial electron microscopy sections[C]. 见:. San Diego, California, United States. 16 - 21 February 2019.

入库方式: OAI收割

来源:自动化研究所

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