期刊
ADVANCED BIOLOGY
卷 -, 期 -, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adbi.202300139
关键词
3D EM; CLEM; machine learning; SBF-SEM
Serial block face scanning electron microscopy (SBF-SEM) is an advanced imaging technique that provides larger x- and y-axis ranges for three-dimensional visualization compared to other volumetric EM techniques. It was developed by Denk and Horstmann in 2004 as a method to resolve the 3D architecture of neuronal networks with nanometer resolution. This article provides an accessible overview of the advantages, challenges, applications, and potential future clinical use of SBF-SEM, as well as alternative AI-based segmentation methods.
Serial block face scanning electron microscopy (SBF-SEM), also referred to as serial block-face electron microscopy, is an advanced ultrastructural imaging technique that enables three-dimensional visualization that provides largerx- and y-axis ranges than other volumetric EM techniques. While SEM is first introduced in the 1930s, SBF-SEM is developed as a novel method to resolve the 3D architecture of neuronal networks across large volumes with nanometer resolution by Denk and Horstmann in 2004. Here, the authors provide an accessible overview of the advantages and challenges associated with SBF-SEM. Beyond this, the applications of SBF-SEM in biochemical domains as well as potential future clinical applications are briefly reviewed. Finally, the alternative forms of artificial intelligence-based segmentation which may contribute to devising a feasible workflow involving SBF-SEM, are also considered.
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