title: 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.
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory.
authors.forename: Wen-Ting
authors.initials: WT
authors.lastname: Tsai
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Hassan
authors.affiliation: Joint Bioenergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory.
authors.forename: Purbasha
authors.initials: P
authors.lastname: Sarkar
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory; National Energy Research Scientific Computing Center, Lawrence Berkeley National Laboratory.
authors.forename: Joaquin
authors.initials: J
authors.lastname: Correa
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory.
authors.forename: Zoltan
authors.initials: Z
authors.lastname: Metlagel
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory.
authors.forename: Danielle M
authors.initials: DM
authors.lastname: Jorgens
authors.affiliation: Life Sciences Division, Lawrence Berkeley National Laboratory; Joint Bioenergy Institute, Physical Biosciences Division, Lawrence Berkeley National Laboratory; mauer@lbl.gov.
authors.forename: Manfred
authors.initials: M
authors.lastname: Auer
mesh_terms: D000465:Algorithms; D017360:Arabidopsis / Q000166:cytology; D002473:Cell Wall / Q000648:ultrastructure; D004847:Epithelial Cells / Q000166:cytology; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D042361:Mammary Glands, Human / Q000166:cytology; D008854:Microscopy, Electron / Q000379:methods*