ACE-HetEM for ab initio Heterogenous Cryo-EM 3D Reconstruction
Weijie Chen Lin Yao Zeqing Xia Yuhang Wang
Due to the extremely low signal-to-noise ratio (SNR) and unknown poses
(projection angles and image translation) in cryo-EM experiments,
reconstructing 3D structures from 2D images is very challenging. On top of
these challenges, heterogeneous cryo-EM reconstruction also has an additional
requirement: conformation classification. An emerging solution to this problem
is called amortized inference, implemented using the autoencoder architecture
or its variants. Instead of searching for the correct
image-to-pose/conformation mapping for every image in the dataset as in
non-amortized methods, amortized inference only needs to train an encoder that
maps images to appropriate latent spaces representing poses or conformations.
Unfortunately, standard amortized-inference-based methods with entangled latent
spaces have difficulty learning the distribution of conformations and poses
from cryo-EM images. In this paper, we propose an unsupervised deep learning
architecture called "ACE-HetEM" based on amortized inference. To explicitly
enforce the disentanglement of conformation classifications and pose
estimations, we designed two alternating training tasks in our method:
image-to-image task and pose-to-pose task. Results on simulated datasets show
that ACE-HetEM has comparable accuracy in pose estimation and produces even
better reconstruction resolution than non-amortized methods. Furthermore, we
show that ACE-HetEM is also applicable to real experimental datasets.