  The prospect of neural reconstruction from Electron Microscopy (EM) images
has been elucidated by the automatic segmentation algorithms. Although
segmentation algorithms eliminate the necessity of tracing the neurons by hand,
significant manual effort is still essential for correcting the mistakes they
make. A considerable amount of human labor is also required for annotating
groundtruth volumes for training the classifiers of a segmentation framework.
It is critically important to diminish the dependence on human interaction in
the overall reconstruction system. This study proposes a novel classifier
training algorithm for EM segmentation aimed to reduce the amount of manual
effort demanded by the groundtruth annotation and error refinement tasks.
Instead of using an exhaustive pixel level groundtruth, an active learning
algorithm is proposed for sparse labeling of pixel and boundaries of
superpixels. Because over-segmentation errors are in general more tolerable and
easier to correct than the under-segmentation errors, our algorithm is designed
to prioritize minimization of false-merges over false-split mistakes. Our
experiments on both 2D and 3D data suggest that the proposed method yields
segmentation outputs that are more amenable to neural reconstruction than those
of existing methods.
