  Two successful approaches for the segmentation of biomedical images are (1)
the selection of segment candidates from a merge-tree, and (2) the clustering
of small superpixels by solving a Multi-Cut problem. In this paper, we
introduce a model that unifies both approaches. Our model, the Candidate
Multi-Cut (CMC), allows joint selection and clustering of segment candidates
from a merge-tree. This way, we overcome the respective limitations of the
individual methods: (1) the space of possible segmentations is not constrained
to candidates of a merge-tree, and (2) the decision for clustering can be made
on candidates larger than superpixels, using features over larger contexts. We
solve the optimization problem of selecting and clustering of candidates using
an integer linear program. On datasets of 2D light microscopy of cell
populations and 3D electron microscopy of neurons, we show that our method
generalizes well and generates more accurate segmentations than merge-tree or
Multi-Cut methods alone.
