The Axon Segmentation Training Initiative for Histology (ASTIH) is a growing collection of publicly available datasets for axon and myelin segmentation in histology images. This initiative aims to foster the development of robust and generalizable deep learning models applicable across diverse biological contexts and imaging techniques. By providing a wide range of data spanning multiple species, microscopy modalities, and resolutions, ASTIH seeks to bridge the gap between fundamental research and translational applications in neuroscience and pathology. We are committed to continuously maintain and expand this collection with new datasets in the future.
Figure 1. Examples of raw and annotated images for every dataset currently online. Scalebars: TEM1: 1 μm; TEM2: 4 μm; SEM1: 30 μm; BF1: (left) 100 μm, (right) 50 μm; BF2: 50 μm.
git clone https://github.com/axondeeepseg/ASTIH.git
cd ASTIH
conda create python==3.12.9 -n astih
conda activate astih
conda env update --file environment.yaml
python scripts/get_data.py --make-splits
python scripts/get_models.py
python scripts/apply_models.py
- A comprehensive, publicly accessible collection of four histology datasets specifically designed for developing and evaluation axon and myelin segmentation models. This collection encompasses unprecedented diversity, featuring over 60,000 manually segmented fibers across three microscopy modalities (TEM, SEM, optical), two species, three regions of the nervous system and various biological conditions.
- Detailed annotations for every datasets with standardized boundary delineation between adjacent fibers, enabling both semantic and instance segmentation tasks.
- Baseline state-of-the-art segmentation models for every dataset, providing a standardized framework for comparative evaluation of future methods. These models are publicly available and can all be used within the AxonDeepSeg open-source software.
We welcome contributions of new data that align with the goals of ASTIH. If you have a relevant dataset you would like to share, please contact us at [email protected]
The code and documentation in this repository are released under the MIT License. The individual datasets within ASTIH may have their own specific licenses, which will be clearly stated in their respective descriptions.
[WIP]