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3D surface reconstruction of cellular cryo-soft X-ray microscopy tomograms using semisupervised deep learning.

Michael C A Dyhr1, Mohsen Sadeghi2, Ralitsa Moynova1

  • 1Institute of Chemistry and Biochemistry, Department of Biology, Chemistry and Pharmacy, Free University of Berlin, 14195 Berlin, Germany.

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Summary

Automated 3D segmentation using deep learning accelerates cell ultrastructure analysis from cryo-soft X-ray tomography (cryo-SXT) data. This method enables high-throughput quantification of nanoscopic cellular features, overcoming manual segmentation bottlenecks.

Keywords:
automated segmentationcryo-soft X-ray microscopydeep learning

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Area of Science:

  • Cellular and Molecular Imaging
  • Biophysics
  • Computational Biology

Background:

  • Cryo-soft X-ray tomography (cryo-SXT) provides high-resolution cellular ultrastructure without labeling.
  • Fast data acquisition in cryo-SXT generates large datasets.
  • Manual segmentation of cryo-SXT data is time-consuming.

Purpose of the Study:

  • To develop an automated 3D segmentation pipeline for cryo-SXT data.
  • To enable high-throughput analysis of cellular ultrastructure.
  • To quantify nanoscopic morphological parameters of cellular structures.

Main Methods:

  • Developed an end-to-end automated 3D segmentation pipeline.
  • Utilized semisupervised deep learning for segmentation.
  • Validated the pipeline on mammalian cell filopodia.

Main Results:

  • The pipeline automates the segmentation of cryo-SXT tomograms.
  • Achieved robust segmentation with limited manual annotations and varying conditions.
  • Enabled extraction of 3D cellular ultrastructure and quantification of filopodia morphology.

Conclusions:

  • Semisupervised deep learning offers an efficient solution for cryo-SXT data segmentation.
  • The automated pipeline facilitates high-throughput analysis of cellular ultrastructure.
  • This approach advances the study of nanoscopic cellular morphology.