Datasets:

Angelou0516
/

BONBID-HIE

license: cc-by-nc-nd-4.0
task_categories:
  - image-segmentation
modality:
  - MRI
language: []
tags:
  - medical-imaging
  - neonatal-brain
  - hypoxic-ischemic-encephalopathy
  - HIE
  - lesion-segmentation
  - diffusion-MRI
  - ADC
  - 3D
pretty_name: BONBID-HIE
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: val
        path: data/val-*
dataset_info:
  features:
    - name: subject_id
      dtype: string
    - name: split
      dtype: string
    - name: num_slices
      dtype: int32
    - name: lesion_voxels
      dtype: int32
    - name: preview_slice_idx
      dtype: int32
    - name: adc_slice
      dtype: image
    - name: zadc_slice
      dtype: image
    - name: lesion_mask
      dtype: image
    - name: overlay
      dtype: image
  splits:
    - name: train
      num_bytes: 1370200
      num_examples: 85
    - name: val
      num_bytes: 42629
      num_examples: 4
  download_size: 1449540
  dataset_size: 1412829

BONBID-HIE

BONBID-HIE (BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy) is a curated MRI dataset for neonatal HIE lesion segmentation, released as part of the MICCAI 2023 BONBID-HIE challenge.

Dataset Summary

Field	Details
Modality	Diffusion MRI (ADC-derived maps)
Body Part	Neonatal brain (term/late-preterm with HIE)
Subjects (Train)	85
Subjects (Val)	4 (Docker sanity-check split)
Subjects (Test)	44 (password-encrypted, not redistributed here)
Format	MetaImage `.mha` (3D volumes)
Total Size	~1.3 GB
Scanners	GE 1.5T Signa, Siemens 3T Trio
License	CC BY-NC-ND 4.0

Data Structure

Each split contains three parallel directories:

1ADC_ss/ — skull-stripped Apparent Diffusion Coefficient map (model input)
2Z_ADC/ — Z-score normalized ADC map (additional input/aid; NOT ground truth)
3LABEL/ — manual expert lesion annotation (recommended ground truth, train/val only)

File naming:

1ADC_ss/MGHNICU_{ID}-VISIT_01-ADC_ss.mha
2Z_ADC/Zmap_MGHNICU_{ID}-VISIT_01-ADC_smooth2mm_clipped10.mha
3LABEL/MGHNICU_{ID}-VISIT_01_lesion.mha

Plus BONBID2023_clinicaldata_val.xlsx (clinical metadata for the val split).

Ground Truth

The recommended ground truth is the manual expert lesion annotation in 3LABEL/, drawn by a trained physician using MRICroN. For 27 uncertain cases, consensus was reached among three pediatric neuroradiologists. The 2Z_ADC/ map is provided as an algorithm-development aid and is NOT a ground-truth annotation.

Notes

Test split is omitted: it was distributed only to MICCAI 2023 challenge participants and is password-encrypted on Zenodo. Training samples (n=85) plus validation (n=4) are reproduced here.
Val split is small (n=4) — intended as a Docker sanity-check, not a statistical validation set. Cross-validation on the train split is the typical evaluation strategy.

Citation

@article{bao2024bonbid,
  title   = {{BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling}},
  author  = {Bao, Rina and Song, Ya'nan and Bates, Sara V. and others},
  journal = {Scientific Data},
  publisher = {Nature},
  year    = {2024},
  doi     = {10.1038/s41597-024-03986-7},
  url     = {https://www.nature.com/articles/s41597-024-03986-7}
}

Source

Original release: Zenodo record 10602767 (V3, paper-cited) Challenge portal: bonbid-hie2023.grand-challenge.org