Learned Watershed: End-to-End Learning of Seeded Segmentation

Steffen Wolf, Lukas Schott, Ullrich Köthe, Fred Hamprecht

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.

Knowledge Graph



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