# End-to-End Learned Random Walker for Seeded Image Segmentation

arXiv: https://arxiv.org/abs/1905.09045 · HTML: https://mlsanity.com/paper/1905.09045/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2019.

## Built on by (most cited first, 4 of 4)

- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Hierarchical Random Walker Segmentation for Large Volumetric Biomedical Images](https://mlsanity.com/paper/2103.09564/) (2021)
- [A Bhattacharyya Coefficient-Based Framework for Noise Model-Aware Random Walker Image Segmentation](https://mlsanity.com/paper/2206.00947/) (2022)
- [BuyTheDips: PathLoss for improved topology-preserving deep learning-based image segmentation](https://mlsanity.com/paper/2207.11446/) (2022)

## Abstract (arXiv)

We present an end-to-end learned algorithm for seeded segmentation. Our method is based on the Random Walker algorithm, where we predict the edge weights of the underlying graph using a convolutional neural network. This can be interpreted as learning context-dependent diffusivities for a linear diffusion process. Besides calculating the exact gradient for optimizing these diffusivities, we also propose simplifications that sparsely sample the gradient and still yield competitive results. The proposed method achieves the currently best results on a seeded version of the CREMI neuron segmentation challenge.