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Updated: Jul 23, 2025

Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Tracing Filaments in Simulated 3D Cryo-Electron Tomography Maps Using a Fast Dynamic Programming Algorithm.

Salim Sazzed1, Peter Scheible1, Jing He1

  • 1Department of Computer Science, Old Dominion University, Norfolk, VA 23529.

Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

We developed a fast dynamic programming method to trace actin filaments in 3D cryo-electron tomography maps. This framework accurately identifies and connects filament segments, improving structural analysis of cellular components.

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Area of Science:

  • Structural Biology
  • Cell Biology
  • Biophysics

Background:

  • Cryo-electron tomography (cryo-ET) is crucial for visualizing subcellular structures.
  • Accurate tracing of actin filaments is essential for understanding cellular processes.
  • Existing methods may struggle with complex 3D arrangements and noise in cryo-ET data.

Purpose of the Study:

  • To present a novel dynamic programming framework for efficient and accurate actin filament tracing in 3D cryo-ET maps.
  • To overcome limitations of existing methods in handling filament bundles and noise.

Main Methods:

  • A dynamic programming algorithm is employed to identify and grow candidate filament segments (CFSs) from seed points.
  • Filament tracing is optimized by aligning tomograms to a dominant direction.
  • CFS length and curvature are tunable parameters.
  • Post-processing includes backward tracing, path density analysis, and collinearity-based fusion to refine and connect segments.

Main Results:

  • The framework successfully traces high-density actin filament segments in various orientations.
  • It effectively handles filaments arranged in bundles.
  • Tunable parameters allow adaptation to different resolutions and feature separations.
  • Noise artifacts are bridged through segment fusion, ensuring continuous filament reconstruction.

Conclusions:

  • The proposed dynamic programming framework offers a fast and robust solution for actin filament tracing in cryo-ET.
  • This method enhances the analysis of cytoskeletal organization and dynamics at the molecular level.
  • Validation on simulated data demonstrates its potential for experimental cryo-ET map analysis.