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Skeleton-based image registration of serial electron microscopy sections
Author(s): Xi Chen; Lijun Shen; Qiwei Xie; Hua Han
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Paper Abstract

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.

Paper Details

Date Published: 18 March 2019
PDF: 6 pages
Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095605 (18 March 2019); doi: 10.1117/12.2512808
Show Author Affiliations
Xi Chen, Institute of Automation (China)
Lijun Shen, Institute of Automation (China)
Qiwei Xie, Institute of Automation (China)
Hua Han, Institute of Automation (China)
Ctr. for Excellence in Brain Science and Intelligence Technology (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 10956:
Medical Imaging 2019: Digital Pathology
John E. Tomaszewski; Aaron D. Ward, Editor(s)

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