Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms

Abstract

Cellular Electron Cryotomography (CryoET) offers the ability to look inside cells and observe macromolecules frozen in action. A primary challenge for this technique is identifying and extracting the molecular components within the crowded cellular environment. We introduce a method using neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yields in-situ structures of molecular components of interest.

21 pages, 8 figures

References in corpus (1)

• Rotation-invariant convolutional neural networks for galaxy morphology prediction

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