  In this paper, we introduce a novel algorithm for segmentation of imperfect
boundary probability maps (BPM) in connectomics. Our algorithm can be a
considered as an extension of spectral clustering. Instead of clustering the
diffusion maps with traditional clustering algorithms, we learn the manifold
and compute an estimate of the minimum normalized cut. We proceed by divide and
conquer. We also introduce a novel criterion for determining if further splits
are necessary in a component based on it's topological properties. Our
algorithm complements the currently popular agglomeration approaches in
connectomics, which overlook the geometrical aspects of this segmentation
problem.
