  The alignment of serial-section electron microscopy (ssEM) images is critical
for efforts in neuroscience that seek to reconstruct neuronal circuits.
However, each ssEM plane contains densely packed structures that vary from one
section to the next, which makes matching features across images a challenge.
Advances in deep learning has resulted in unprecedented performance in similar
computer vision problems, but to our knowledge, they have not been successfully
applied to ssEM image co-registration. In this paper, we introduce a novel deep
network model that combines a spatial transformer for image deformation and a
convolutional autoencoder for unsupervised feature learning for robust ssEM
image alignment. This results in improved accuracy and robustness while
requiring substantially less user intervention than conventional methods. We
evaluate our method by comparing registration quality across several datasets.
