# Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics

arXiv: https://arxiv.org/abs/1812.01157 · HTML: https://mlsanity.com/paper/1812.01157/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions](https://mlsanity.com/paper/2107.05451/) (2021)
- [RLCorrector: Reinforced Proofreading for Cell-level Microscopy Image Segmentation](https://mlsanity.com/paper/2106.05487/) (2021)
- [Learning Guided Electron Microscopy with Active Acquisition](https://mlsanity.com/paper/2101.02746/) (2021)
- [A Topology-Attention ConvLSTM Network and Its Application to EM Images](https://mlsanity.com/paper/2202.03430/) (2022)
- [NeuroMamba: Multi-Perspective Feature Interaction with Visual Mamba for Neuron Segmentation](https://mlsanity.com/paper/2601.15929/) (2026)
- [NeuroADDA: Active Discriminative Domain Adaptation in Connectomic](https://mlsanity.com/paper/2503.06196/) (2025)

## Abstract (arXiv)

Pixel-accurate tracking of objects is a key element in many computer vision applications, often solved by iterated individual object tracking or instance segmentation followed by object matching. Here we introduce cross-classification clustering (3C), a technique that simultaneously tracks complex, interrelated objects in an image stack. The key idea in cross-classification is to efficiently turn a clustering problem into a classification problem by running a logarithmic number of independent classifications per image, letting the cross-labeling of these classifications uniquely classify each pixel to the object labels. We apply the 3C mechanism to achieve state-of-the-art accuracy in connectomics -- the nanoscale mapping of neural tissue from electron microscopy volumes. Our reconstruction system increases scalability by an order of magnitude over existing single-object tracking methods (such as flood-filling networks). This scalability is important for the deployment of connectomics pipelines, since currently the best performing techniques require computing infrastructures that are beyond the reach of most laboratories. Our algorithm may offer benefits in other domains that require pixel-accurate tracking of multiple objects, such as segmentation of videos and medical imagery.