  Volumetric (3d) images are acquired for many scientific and biomedical
purposes using imaging methods such as serial section microscopy, CT scans, and
MRI. A frequent step in the analysis and reconstruction of such data is the
alignment and registration of images that were acquired in succession along a
spatial or temporal dimension. For example, in serial section electron
microscopy, individual 2d sections are imaged via electron microscopy and then
must be aligned to one another in order to produce a coherent 3d volume. State
of the art approaches find image correspondences derived from patch matching
and invariant feature detectors, and then solve optimization problems that
rigidly or elastically deform series of images into an aligned volume. Here we
show how fully convolutional neural networks trained with an adversarial loss
function can be used for two tasks: (1) synthesis of missing or damaged image
data from adjacent sections, and (2) fine-scale alignment of block-face
electron microscopy data. Finally, we show how these two capabilities can be
combined in order to produce artificial isotropic volumes from anisotropic
image volumes using a super-resolution adversarial alignment and interpolation
approach.
