Datasets:
Tasks:
Image Segmentation
Size:
1K - 10K
ArXiv:
Tags:
medical-imaging
electron-microscopy
neuroscience
axon-segmentation
3d-segmentation
connectomics
License:
File size: 3,594 Bytes
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license: cc-by-4.0
task_categories:
- image-segmentation
tags:
- medical-imaging
- electron-microscopy
- neuroscience
- axon-segmentation
- 3d-segmentation
- connectomics
size_categories:
- 1K<n<10K
---
# AxonEM Dataset
Large-scale 3D Axon Instance Segmentation of Brain Cortical Regions from serial section Electron Microscopy (sEM).
## Dataset Description
AxonEM contains high-resolution electron microscopy volumes of mouse and human brain cortex tissue for axon instance segmentation.
### Subsets
| Subset | Species | Volumes | Resolution | Original Size |
|--------|---------|---------|------------|---------------|
| Human | Homo sapiens | 9 | 30×8×8 nm | 1000×4096×4096 |
| Mouse | Mus musculus | 9 | 40×8×8 nm | 750×4096×4096 |
### Volume Information
Each training sub-volume has shape **(90, 1536, 1536)** voxels with padding:
- Padding: 20 slices in Z, 512 pixels in Y/X (on each side)
- Annotated region: (50, 512, 512) after removing padding
### File Structure
```
AxonEM/
├── EM30-H-train-9vol-pad-20-512-512/ # Human subset
│ ├── im_0-0-0_pad.h5 # Image volume
│ ├── seg_0-0-0_pad.h5 # Segmentation (instance labels)
│ └── ...
├── EM30-M-train-9vol-pad-20-512-512/ # Mouse subset
│ ├── im_0-0-0_pad.h5
│ ├── seg_0-0-0_pad.h5
│ ├── valid_mask.h5 # Valid annotation mask
│ └── ...
└── README.md
```
### HDF5 Format
Each `.h5` file contains a single dataset with key `'main'`:
- **Image files** (`im_*.h5`): uint8 grayscale EM images, shape (90, 1536, 1536)
- **Segmentation files** (`seg_*.h5`): uint8 instance labels, shape (90, 1536, 1536)
- 0 = background
- 1-N = axon instance IDs
### Loading Example
```python
import h5py
import numpy as np
# Load a volume
with h5py.File('EM30-H-train-9vol-pad-20-512-512/im_0-0-0_pad.h5', 'r') as f:
image = f['main'][:] # (90, 1536, 1536) uint8
with h5py.File('EM30-H-train-9vol-pad-20-512-512/seg_0-0-0_pad.h5', 'r') as f:
labels = f['main'][:] # (90, 1536, 1536) uint8
# Convert to binary mask (axon vs background)
binary_mask = (labels > 0).astype(np.uint8)
# Remove padding to get annotated region
z_pad, y_pad, x_pad = 20, 512, 512
image_cropped = image[z_pad:-z_pad, y_pad:-y_pad, x_pad:-x_pad] # (50, 512, 512)
```
### Using with EasyMedSeg
```python
from dataloader.axonem import AxonEMImageDataset, AxonEMVideoDataset
# Image mode (2D slices)
dataset = AxonEMImageDataset(
hf_repo_id="Angelou0516/AxonEM",
subset="human", # or "mouse"
)
# Video mode (3D volumes)
dataset = AxonEMVideoDataset(
hf_repo_id="Angelou0516/AxonEM",
subset="human",
)
```
## Citation
```bibtex
@inproceedings{wei2021miccai,
title={AxonEM Dataset: 3D Axon Instance Segmentation of Brain Cortical Regions},
author={Wei, Donglai and Xu, Kisuk and Liao, Ran and Pfister, Hanspeter and
Haehn, Daniel and Bhanu, Shubham and Bhattacharyya, Chandrajit},
booktitle={International Conference on Medical Image Computing and
Computer-Assisted Intervention (MICCAI)},
year={2021}
}
```
## Links
- [Grand Challenge](https://axonem.grand-challenge.org/)
- [arXiv Paper](https://arxiv.org/abs/2107.05451)
- [PyTorch Connectomics](https://github.com/zudi-lin/pytorch_connectomics)
## License
This dataset is released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/).
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