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

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High resolution electron tomography and segmentation-by-modeling interpretation in Bsoft.

J Bernard Heymann¹

¹Laboratory for Structural Biology Research, National Institute of Arthritis, Musculoskeletal and Skin Diseases, NIH, Bethesda, Maryland, USA.

Protein Science : a Publication of the Protein Society

|August 28, 2020

Summary

Bsoft now automates cryo-electron tomography (cryo-ET) alignment and reconstruction. New methods enhance specimen thickness estimation and enable fiducialless alignment for improved high-resolution imaging.

Keywords:

3D reconstruction contrast transfer function cryo-electron microscopy image alignment modeling particle picking segmentation specimen thickness determination subtomogram averaging

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Last Updated: Dec 10, 2025

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Published on: August 13, 2014

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Deep Learning-Based Segmentation of Cryo-Electron Tomograms

Published on: November 11, 2022

Area of Science:

Structural biology
Electron microscopy
Biophysics

Background:

Bsoft provides tools for processing tomographic tilt series and interpreting tomograms.
Advancements in direct detector technology enable more efficient and higher-quality data collection in cryo-electron tomography (cryo-ET).
Progress towards automation in cryo-ET is crucial for handling the increasing complexity and volume of data.

Purpose of the Study:

To refine existing algorithms and develop new ones for automated tilt series alignment and reconstruction in Bsoft.
To improve the accuracy and efficiency of cryo-ET data processing.
To facilitate high-resolution structural analysis through advanced image correction techniques.

Main Methods:

Implementation of specimen thickness estimation and fiducialless alignment methods.
Development of a novel method for contrast transfer function (CTF) correction by generating a power spectrum from the whole micrograph, compensating for specimen tilting.
Integration of CTF determination and correction into the reconstruction process.

Main Results:

Successful augmentation of existing fiducial-based alignment with fiducialless alignment capabilities.
A new method for CTF correction applicable to tilted specimens in cryo-ET.
Enhanced automation of tilt series alignment and reconstruction, paving the way for more efficient cryo-ET workflows.

Conclusions:

The updated Bsoft tools significantly advance the automation of cryo-ET data processing, from tilt series alignment to reconstruction.
The developed CTF correction method addresses a key challenge in high-resolution cryo-ET.
While reconstruction is becoming automated, tomogram interpretation remains a significant, less automated step.