# Machine learning of hierarchical clustering to segment 2D and 3D images

arXiv: https://arxiv.org/abs/1303.6163 · HTML: https://mlsanity.com/paper/1303.6163/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2013.

## Built on by (most cited first, 11 of 11)

- [Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation](https://mlsanity.com/paper/2306.04811/) (2023)
- [Self-Supervised Neuron Segmentation with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.04148/) (2023)
- [Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation](https://mlsanity.com/paper/2308.09917/) (2023)
- [AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions](https://mlsanity.com/paper/2107.05451/) (2021)
- [Enhancing Boundary Segmentation for Topological Accuracy with Skeleton-based Methods](https://mlsanity.com/paper/2404.18539/) (2024)
- [Large-Scale Multi-Hypotheses Cell Tracking Using Ultrametric Contours Maps](https://mlsanity.com/paper/2308.04526/) (2023)
- [TokenUnify: Scaling Up Autoregressive Pretraining for Neuron Segmentation](https://mlsanity.com/paper/2405.16847/) (2024)
- [Does DINOv3 Set a New Medical Vision Standard? Benchmarking 2D and 3D Classification, Segmentation, and Registration](https://mlsanity.com/paper/2509.06467/) (2025)
- [A Topology-Attention ConvLSTM Network and Its Application to EM Images](https://mlsanity.com/paper/2202.03430/) (2022)
- [Complex Wavelet Mutual Information Loss: A Multi-Scale Loss Function for Semantic Segmentation](https://mlsanity.com/paper/2502.00563/) (2025)
- [Steerable Pyramid Weighted Loss: Multi-Scale Adaptive Weighting for Semantic Segmentation](https://mlsanity.com/paper/2503.06604/) (2025)

## Abstract (arXiv)

We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.