Datasets:
Tasks:
Image Segmentation
Size:
< 1K
Tags:
medical-imaging
neonatal-brain
hypoxic-ischemic-encephalopathy
HIE
lesion-segmentation
diffusion-MRI
License:
File size: 3,566 Bytes
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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
```bibtex
@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](https://zenodo.org/records/10602767) (V3, paper-cited)
Challenge portal: [bonbid-hie2023.grand-challenge.org](https://bonbid-hie2023.grand-challenge.org/)
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