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

## TL;DR

This paper introduces a novel end-to-end neural network approach that jointly learns boundary maps and watershed segmentation, achieving state-of-the-art results on a challenging biomedical segmentation task.

## Contribution

It is the first to train watershed computation jointly with boundary map prediction using a convolutional-recurrent neural network.

## Key findings

- Achieved best known results on CREMI segmentation challenge.
- Demonstrated the effectiveness of joint learning for watershed-based segmentation.
- Outperformed previous methods using learned boundary maps.

## 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.