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

Steffen Wolf, Lukas Schott, Ullrich Kothe, Fred Hamprecht; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2011-2019

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 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.

Related Material

[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Wolf_2017_ICCV,
author = {Wolf, Steffen and Schott, Lukas and Kothe, Ullrich and Hamprecht, Fred},
title = {Learned Watershed: End-To-End Learning of Seeded Segmentation},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}