# AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions

arXiv: https://arxiv.org/abs/2107.05451 · HTML: https://mlsanity.com/paper/2107.05451/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 39 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Superhuman Accuracy on the SNEMI3D Connectomics Challenge](https://mlsanity.com/paper/1706.00120/) (2017)
- [Machine learning of hierarchical clustering to segment 2D and 3D images](https://mlsanity.com/paper/1303.6163/) (2013)
- [AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks](https://mlsanity.com/paper/1711.01004/) (2017)
- [Cross-Classification Clustering: An Efficient Multi-Object Tracking Technique for 3-D Instance Segmentation in Connectomics](https://mlsanity.com/paper/1812.01157/) (2018)
- [Convolutional nets for reconstructing neural circuits from brain images acquired by serial section electron microscopy](https://mlsanity.com/paper/1904.12966/) (2019)
- [Image Segmentation by Size-Dependent Single Linkage Clustering of a Watershed Basin Graph](https://mlsanity.com/paper/1505.00249/) (2015)
- [A Multi-Pass Approach to Large-Scale Connectomics](https://mlsanity.com/paper/1612.02120/) (2016)
- [Consistent Recurrent Neural Networks for 3D Neuron Segmentation](https://mlsanity.com/paper/2102.01021/) (2021)
- [Toward an Automated HPC Pipeline for Processing Large Scale Electron Microscopy Data](https://mlsanity.com/paper/2011.03204/) (2020)

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

- [MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction](https://mlsanity.com/paper/2301.00345/) (2023)
- [Learning Multimodal Volumetric Features for Large-Scale Neuron Tracing](https://mlsanity.com/paper/2401.03043/) (2024)
- [PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics](https://mlsanity.com/paper/2112.05754/) (2021)
- [AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus Callosum cross section from EM Images](https://mlsanity.com/paper/2307.02464/) (2023)
- [FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures](https://mlsanity.com/paper/2404.00130/) (2024)
- [A Survey of Medical Point Cloud Shape Learning: Registration, Reconstruction and Variation](https://mlsanity.com/paper/2508.03057/) (2025)
- [Fully Automated Segmentation of Fiber Bundles in Anatomic Tracing Data](https://mlsanity.com/paper/2508.12942/) (2025)
- [The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons](https://mlsanity.com/paper/2302.03819/) (2023)
- [Tractography-Driven Synthetic Data Generation for Fiber Bundle Segmentation in Tracer Histology](https://mlsanity.com/paper/2606.26898/) (2026)
- [Frenet-Serret Frame-based Decomposition for Part Segmentation of 3D Curvilinear Structures](https://mlsanity.com/paper/2404.14435/) (2024)
- [SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images](https://mlsanity.com/paper/2607.22332/) (2026)

## Abstract (arXiv)

Electron microscopy (EM) enables the reconstruction of neural circuits at the level of individual synapses, which has been transformative for scientific discoveries. However, due to the complex morphology, an accurate reconstruction of cortical axons has become a major challenge. Worse still, there is no publicly available large-scale EM dataset from the cortex that provides dense ground truth segmentation for axons, making it difficult to develop and evaluate large-scale axon reconstruction methods. To address this, we introduce the AxonEM dataset, which consists of two 30x30x30 um^3 EM image volumes from the human and mouse cortex, respectively. We thoroughly proofread over 18,000 axon instances to provide dense 3D axon instance segmentation, enabling large-scale evaluation of axon reconstruction methods. In addition, we densely annotate nine ground truth subvolumes for training, per each data volume. With this, we reproduce two published state-of-the-art methods and provide their evaluation results as a baseline. We publicly release our code and data at https://connectomics-bazaar.github.io/proj/AxonEM/index.html to foster the development of advanced methods.