  We define and study error detection and correction tasks that are useful for
3D reconstruction of neurons from electron microscopic imagery, and for image
segmentation more generally. Both tasks take as input the raw image and a
binary mask representing a candidate object. For the error detection task, the
desired output is a map of split and merge errors in the object. For the error
correction task, the desired output is the true object. We call this object
mask pruning, because the candidate object mask is assumed to be a superset of
the true object. We train multiscale 3D convolutional networks to perform both
tasks. We find that the error-detecting net can achieve high accuracy. The
accuracy of the error-correcting net is enhanced if its input object mask is
"advice" (union of erroneous objects) from the error-detecting net.
