  Cellular Electron Cryo-Tomography (CECT) is a powerful imaging technique for
the 3D visualization of cellular structure and organization at submolecular
resolution. It enables analyzing the native structures of macromolecular
complexes and their spatial organization inside single cells. However, due to
the high degree of structural complexity and practical imaging limitations,
systematic macromolecular structural recovery inside CECT images remains
challenging. Particularly, the recovery of a macromolecule is likely to be
biased by its neighbor structures due to the high molecular crowding. To reduce
the bias, here we introduce a novel 3D convolutional neural network inspired by
Fully Convolutional Network and Encoder-Decoder Architecture for the supervised
segmentation of macromolecules of interest in subtomograms. The tests of our
models on realistically simulated CECT data demonstrate that our new approach
has significantly improved segmentation performance compared to our baseline
approach. Also, we demonstrate that the proposed model has generalization
ability to segment new structures that do not exist in training data.
