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キーワード:
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要旨:
Macromolecular structure determination using cryo-electron
tomography requires large amount of subtomograms
depicting the same molecule, which are averaged. In this
paper, we propose a novel automatic particle picking and
classification method for cryo-electron tomograms. The
workflow comprises two stages: detection and classification.
The detection method consists of a template-free picking
procedure based on anisotropic diffusion filtering and
connected component analysis. For classification, a novel
3D rotation invariant feature descriptor named Sphere Ring
Haar and a hierarchical classification algorithm consisting
of two machine learning models (DBSCAN and random
forest) are proposed. The performance of our method is
superior compared to template matching based methods and
we achieved over 90% true positive rates for detection of
proteasomes and ribosomes in experimental data.
