Translator Disclaimer
Presentation + Paper
18 March 2019 Skeleton-based image registration of serial electron microscopy sections
Author Affiliations +
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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xi Chen, Lijun Shen, Qiwei Xie, and Hua Han "Skeleton-based image registration of serial electron microscopy sections", Proc. SPIE 10956, Medical Imaging 2019: Digital Pathology, 1095605 (18 March 2019);

Back to Top