# AxonDeepSeg: automatic axon and myelin segmentation from microscopy data using convolutional neural networks

arXiv: https://arxiv.org/abs/1711.01004 · HTML: https://mlsanity.com/paper/1711.01004/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Nov 2017.

## 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)
- [AxonCallosumEM Dataset: Axon Semantic Segmentation of Whole Corpus Callosum cross section from EM Images](https://mlsanity.com/paper/2307.02464/) (2023)
- [An overview of open source Deep Learning-based libraries for Neuroscience](https://mlsanity.com/paper/2301.05057/) (2022)
- [The XPRESS Challenge: Xray Projectomic Reconstruction -- Extracting Segmentation with Skeletons](https://mlsanity.com/paper/2302.03819/) (2023)
- [Multi-Domain Data Aggregation for Axon and Myelin Segmentation in Histology Images](https://mlsanity.com/paper/2409.11552/) (2024)
- [AxonSynth: Domain-Randomized Synthetic Data for Zero-Shot 3D Axon Segmentation in Light-Sheet Microscopy](https://mlsanity.com/paper/2609.31431/) (2026)

## Abstract (arXiv)

Segmentation of axon and myelin from microscopy images of the nervous system provides useful quantitative information about the tissue microstructure, such as axon density and myelin thickness. This could be used for instance to document cell morphometry across species, or to validate novel non-invasive quantitative magnetic resonance imaging techniques. Most currently-available segmentation algorithms are based on standard image processing and usually require multiple processing steps and/or parameter tuning by the user to adapt to different modalities. Moreover, only few methods are publicly available. We introduce AxonDeepSeg, an open-source software that performs axon and myelin segmentation of microscopic images using deep learning. AxonDeepSeg features: (i) a convolutional neural network architecture; (ii) an easy training procedure to generate new models based on manually-labelled data and (iii) two ready-to-use models trained from scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Results show high pixel-wise accuracy across various species: 85% on rat SEM, 81% on human SEM, 95% on mice TEM and 84% on macaque TEM. Segmentation of a full rat spinal cord slice is computed and morphological metrics are extracted and compared against the literature. AxonDeepSeg is freely available at https://github.com/neuropoly/axondeepseg