Learned Watershed: End-to-End Learning of Seeded Segmentation
Computer Vision and Pattern Recognition
2017-09-05 v2
Abstract
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.
Cite
@article{arxiv.1704.02249,
title = {Learned Watershed: End-to-End Learning of Seeded Segmentation},
author = {Steffen Wolf and Lukas Schott and Ullrich Köthe and Fred Hamprecht},
journal= {arXiv preprint arXiv:1704.02249},
year = {2017}
}
Comments
The first two authors contributed equally