  Cellular electron cryo-tomography enables the 3D visualization of cellular
organization in the near-native state and at submolecular resolution. However,
the contents of cellular tomograms are often complex, making it difficult to
automatically isolate different in situ cellular components. In this paper, we
propose a convolutional autoencoder-based unsupervised approach to provide a
coarse grouping of 3D small subvolumes extracted from tomograms. We demonstrate
that the autoencoder can be used for efficient and coarse characterization of
features of macromolecular complexes and surfaces, such as membranes. In
addition, the autoencoder can be used to detect non-cellular features related
to sample preparation and data collection, such as carbon edges from the grid
and tomogram boundaries. The autoencoder is also able to detect patterns that
may indicate spatial interactions between cellular components. Furthermore, we
demonstrate that our autoencoder can be used for weakly supervised semantic
segmentation of cellular components, requiring a very small amount of manual
annotation.
