  Cellular processes are governed by macromolecular complexes inside the cell.
Study of the native structures of macromolecular complexes has been extremely
difficult due to lack of data. With recent breakthroughs in Cellular electron
cryo tomography (CECT) 3D imaging technology, it is now possible for
researchers to gain accesses to fully study and understand the macromolecular
structures single cells. However, systematic recovery of macromolecular
structures from CECT is very difficult due to high degree of structural
complexity and practical imaging limitations. Specifically, we proposed a deep
learning based image classification approach for large-scale systematic
macromolecular structure separation from CECT data. However, our previous work
was only a very initial step towards exploration of the full potential of deep
learning based macromolecule separation. In this paper, we focus on improving
classification performance by proposing three newly designed individual CNN
models: an extended version of (Deep Small Receptive Field) DSRF3D, donated as
DSRF3D-v2, a 3D residual block based neural network, named as RB3D and a
convolutional 3D(C3D) based model, CB3D. We compare them with our previously
developed model (DSRF3D) on 12 datasets with different SNRs and tilt angle
ranges. The experiments show that our new models achieved significantly higher
classification accuracies. The accuracies are not only higher than 0.9 on
normal datasets, but also demonstrate potentials to operate on datasets with
high levels of noises and missing wedge effects presented.
