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

Steffen Wolf, Lukas Schott, Ullrich Kothe, Fred Hamprecht; 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.

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[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 = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}