Join

BONBID-HIE Lesion Segmentation

Data

The complete data is organized in the format shown below. 

BONBID-HIE provides, per patient for MICCAI 2023 Challenge lesion prediction: 

  • 1ADC_ss: skull stripped Apparent Diffusion Coefficient (ADC) map. 
  • 2Z_ADC: ZADC map.
  • 3LABEL: expert lesion annotations.

For data descriptions, please read and cite our style="" target="_blank">paper.  

Rina Bao, Ya'nan Song, Sara V. Bates, Rebecca J. Weiss, Anna N. Foster, Camilo Jaimes Cobos, Susan Sotardi, Yue Zhang, Randy L. Gollub, P. Ellen Grant, Yangming Ou "BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation". bioRxiv 2023.06.30.546841; doi: https://doi.org/10.1101/2023.06.30.546841

Training Set

Training data download zenodo

N=85 cases Training data is used to train models and evaluate performance. We strongly encourage participating teams to do cross-validation and submit prediction results to our leaderboard.

We released the 1ADC_ss, 2Z_ADC, 3LABEL. 

Validation Set

Validation data

N=4 cases The small validation set is only used for participating teams to do a sanity check of algorithm dockers. The performance won't be used to rank teams.

Testing Set

Testing data (hidden set)

N=44 cases The algorithm dockers submitted will be run on the test set. The final performance and ranks will be evaluated on the test set.

License

All training data has been made publicly available under the CC BY NC ND license (https://creativecommons.org/licenses/by-nc-nd/2.0/, allowing academic use with credit, prohibiting commercial use without owner’s permission, and disallowing derivation or adaption of data).