# Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph

arXiv: https://arxiv.org/abs/1505.00249 · HTML: https://mlsanity.com/paper/1505.00249/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2015.

## Built on by (most cited first, 5 of 5)

- [NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale](https://mlsanity.com/paper/2107.05840/) (2021)
- [AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions](https://mlsanity.com/paper/2107.05451/) (2021)
- [Consistent Recurrent Neural Networks for 3D Neuron Segmentation](https://mlsanity.com/paper/2102.01021/) (2021)
- [Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN](https://mlsanity.com/paper/2204.03082/) (2022)
- [Large-scale image segmentation based on distributed clustering algorithms](https://mlsanity.com/paper/2106.10795/) (2021)

## Abstract (arXiv)

We present a method for hierarchical image segmentation that defines a disaffinity graph on the image, over-segments it into watershed basins, defines a new graph on the basins, and then merges basins with a modified, size-dependent version of single linkage clustering. The quasilinear runtime of the method makes it suitable for segmenting large images. We illustrate the method on the challenging problem of segmenting 3D electron microscopic brain images.