From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Abstract

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam-and serial block face-scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.

Quantifying Variability of Manual Annotation in Cryo-Electron Tomograms

“…In addition to being time-consuming, it is generally well accepted in the cryoET field that manual segmentation is error-prone, subjective and variable (Lučić et al, 2005, 2013; Sandberg, 2007; Volkmann, 2010; Tsai et al, 2014). …”

A guide to analysis and reconstruction of serial block face scanning electron microscopy data

“…In doing so, we aim to provide direction to researchers new to SBF-SEM by drawing attention to advantages and limitations of the software packages and tools. A similar comparison was done by Tsai et al (2014), which details data reconstruction from multiple volume EM techniques. However, the in-depth workflow from these techniques may appear daunting for those new to image analyses.…”

“…A similar comparison was done by Tsai et al . (2014), which details data reconstruction from multiple volume EM techniques. However, the in‐depth workflow from these techniques may appear daunting for those new to image analyses.…”

SuRVoS: Super-Region Volume Segmentation workbench

“…Recent cryoET and FIB-SEM studies have used various filtering and segmentation methods to ease analysis of their datasets (Prill et al, 2013, Tsai et al, 2014, Vidavsky et al, 2016, Vyas et al, 2016). However, in many cases manual segmentation and basic thresholding methods are still routinely used.…”