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

Abstract · Apr 7, 2017 14:40 ·

watershed altitude boundary seeds region topographic seed false assignment cs-cv

Arxiv Abstract

  • 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.

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