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README.md

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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/).