Hugging Face's logo

Datasets:
Spirit-26
/
BraTS-2024-Complete

Tasks:
Image Segmentation
Languages:
English
Size:
10B<n<100B
Tags:
medical-imaging
brain-tumor
segmentation
mri
brats-2024
brats
License:
Dataset card Files
xet
 Community
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ImportError
Message:      To support decoding NIfTI files, please install 'nibabel'.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2543, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2061, in __iter__
                  batch = formatter.format_batch(pa_table)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 472, in format_batch
                  batch = self.python_features_decoder.decode_batch(batch)
                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/formatting/formatting.py", line 234, in decode_batch
                  return self.features.decode_batch(batch, token_per_repo_id=self.token_per_repo_id) if self.features else batch
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 2161, in decode_batch
                  decode_nested_example(self[column_name], value, token_per_repo_id=token_per_repo_id)
                File "/usr/local/lib/python3.12/site-packages/datasets/features/features.py", line 1419, in decode_nested_example
                  return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/features/nifti.py", line 172, in decode_example
                  raise ImportError("To support decoding NIfTI files, please install 'nibabel'.")
              ImportError: To support decoding NIfTI files, please install 'nibabel'.

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

BraTS 2024 Complete Prepared Dataset

Brain Tumor Segmentation (Leave a like 💖 if this helped you)

Dataset Description

This is an organized and verified version of the BraTS 2024 challenge datasets, including three tumor types.

Included Datasets

Dataset Type Cases Source
BraTS-GLI Glioma 1,809 Synapse (Dec 2024)
BraTS-MEN-RT Meningioma + RT 571 Synapse (Feb 2025)
BraTS-PED Pediatric 348 Cancer Imaging Archive

Total: 2,728 multi-parametric MRI cases

Structure 

BraTS-2024-Complete/
├── BraTS-GLI/
│   ├── train/ # 1,621 cases
│   └── val/ # 188 cases
├── BraTS-MEN-RT/
│   ├── train/ # 500 cases
│   ├── train_additional/# 1 case
│   └── val/ # 70 cases
└── BraTS-PED/
    ├── train/ # 257 cases
    └── val/ # 91 cases

Each patient folder contains:

  • T1-weighted (t1n.nii.gz)
  • T1-contrast enhanced (t1c.nii.gz)
  • T2-weighted (t2w.nii.gz)
  • T2-FLAIR (t2f.nii.gz)
  • Segmentation mask (seg.nii.gz or gtv.nii.gz)

Dataset Features

  • Verified integrity - all files load correctly
  • Clean split - train/val already separated
  • Complete metadata - all clinical files included
  • Ready-to-use - organized for immediate training

Metadata Included

BraTS-PED

  • BraTS-PEDs_metadata.tsv - Patient demographics (age, sex, institution, survival)
  • BraTS-PEDs_Imaging_Info.tsv - MRI technical parameters per scan

BraTS-GLI

  • BraTS-PTG supplementary demographic information and metadata.xlsx
  • CITATIONS.bib

BraTS-MEN-RT

  • Meningioma radiotherapy supplementary clinical data.xlsx
  • CITATION.bib

License Information

This dataset combines three collections with different terms. See LICENSES.md for complete details.

Collection License Summary
BraTS-GLI Synapse Terms Research only, must cite
BraTS-MEN-RT Synapse Terms Research only, must cite
BraTS-PED CC BY-NC 4.0 Share + adapt with attribution, non-commercial

Citation 

If you use this organized dataset, please cite:
@dataset{yourname_2025_brats2024complete,
author = {Your Name},
title = {BraTS-2024-Complete},
year = 2025,
publisher = {Hugging Face},
version = {1.0.0},
url = {https://huggingface.co/datasets/yourusername/BraTS-2024-Complete}
}

Additionally, cite the original BraTS papers as provided in the respective CITATION files.

Acknowledgments

  • BraTS 2024 Organizers
  • RSNA, ASNR, MICCAI
  • The Cancer Imaging Archive (TCIA)
  • All contributing institutions
Downloads last month
10,514