Datasets:

Angelou0516
/

AxonEM

Error code:   InfoError
Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/hdf5/hdf5.py", line 64, in _split_generators
                  with h5py.File(first_file, "r") as h5:
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/files.py", line 564, in __init__
                  fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/h5py/_hl/files.py", line 238, in make_fid
                  fid = h5f.open(name, flags, fapl=fapl)
                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "h5py/_objects.pyx", line 56, in h5py._objects.with_phil.wrapper
                File "h5py/_objects.pyx", line 57, in h5py._objects.with_phil.wrapper
                File "h5py/h5f.pyx", line 102, in h5py.h5f.open
              FileNotFoundError: [Errno 2] Unable to synchronously open file (unable to open file: name = 'hf://datasets/Angelou0516/AxonEM@e7fc28b2e8a224e8f8920ac4364fe5ad0996b1a8/EM30-H-train-9vol-pad-20-512-512/im_0-0-0_pad.h5', errno = 2, error message = 'No such file or directory', flags = 0, o_flags = 0)
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 223, in compute_first_rows_from_streaming_response
                  info = get_dataset_config_info(path=dataset, config_name=config, token=hf_token)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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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

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

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

@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}
}