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

@article{Wolf2017LearnedWE,
  title={Learned Watershed: End-to-End Learning of Seeded Segmentation},
  author={Steffen Wolf and Lukas Schott and U. K{\"o}the and Fred A. Hamprecht},
  journal={2017 IEEE International Conference on Computer Vision (ICCV)},
  year={2017},
  pages={2030-2038}
}
Learned boundary maps are known to outperform handcrafted ones as a basis for the watershed algorithm. [] Key Method The estimator for the merging priorities is cast as a neural network that is convolutional (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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