Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph
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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.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 May 2015.
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Citing papers per year: 2021: 4; 2022: 1.
5 papers.
- 2021NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scalecited by 12
- 2021AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regionscited by 11
- 2021Consistent Recurrent Neural Networks for 3D Neuron Segmentationcited by 3
- 2022Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GANcited by 3
- 2021Large-scale image segmentation based on distributed clustering algorithmscited by 0
- 2022Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN6 Apr 2022
- 2021NucMM Dataset: 3D Neuronal Nuclei Instance Segmentation at Sub-Cubic Millimeter Scale13 Jul 2021
- 2021AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions12 Jul 2021
- 2021Large-scale image segmentation based on distributed clustering algorithms21 Jun 2021
- 2021Consistent Recurrent Neural Networks for 3D Neuron Segmentation1 Feb 2021