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Updated: May 29, 2026

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
Published on: November 11, 2022
Automated segmentation of electron tomograms for a quantitative description of actin filament networks
Alexander Rigort1, David Günther, Reiner Hegerl
1Max Planck Institute of Biochemistry, Department of Structural Biology, Am Klopferspitz 18, D-82152 Martinsried, Germany. rigort@biochem.mpg.de
Abstract:
Cryo-electron tomography allows to visualize individual actin filaments and to describe the three-dimensional organization of actin networks in the context of unperturbed cellular environments. For a quantitative characterization of actin filament networks, the tomograms must be segmented in a reproducible manner. Here, we describe an automated procedure for the segmentation of actin filaments, which combines template matching with a new tracing algorithm. The result is a set of lines, each one representing the central line of a filament. As demonstrated with cryo-tomograms of cellular actin networks, these line sets can be used to characterize filament networks in terms of filament length, orientation, density, stiffness (persistence length), or the occurrence of branching points.
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