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Cellular electron cryo-tomography enables the 3D visualization of cellularorganization in the near-native state and at submolecular resolution. However,the contents of cellular tomograms are often complex, making it difficult toautomatically isolate different in situ cellular components. In this paper, wepropose a convolutional autoencoder-based unsupervised approach to provide acoarse grouping of 3D small subvolumes extracted from tomograms. We demonstratethat the autoencoder can be used for efficient and coarse characterization offeatures of macromolecular complexes and surfaces, such as membranes. Inaddition, the autoencoder can be used to detect non-cellular features relatedto sample preparation and data collection, such as carbon edges from the gridand tomogram boundaries. The autoencoder is also able to detect patterns thatmay indicate spatial interactions between cellular components. Furthermore, wedemonstrate that our autoencoder can be used for weakly supervised semanticsegmentation of cellular components, requiring a very small amount of manualannotation.

A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation