  Learned boundary maps are known to outperform hand- crafted ones as a basis
for the watershed algorithm. We show, for the first time, how to train
watershed computation jointly with boundary map prediction. The estimator for
the merging priorities is cast as a neural network that is con- volutional
(over space) and recurrent (over iterations). The latter allows learning of
complex shape priors. The method gives the best known seeded segmentation
results on the CREMI segmentation challenge.
