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BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy

  • Rina Bao*
  • , Anna N. Foster
  • , Ya'Nan Song
  • , Rutvi Vyas
  • , Ankush Kesri
  • , Imad Eddine Toubal
  • , Elham Soltani Kazemi
  • , Gani Rahmon
  • , Taci Kucukpinar
  • , Mohamed Almansour
  • , Mai Lan Ho
  • , K. Palaniappan
  • , Dean Ninalga
  • , Chiranjeewee Prasad Koirala
  • , Sovesh Mohapatra
  • , Gottfried Schlaug
  • , Marek Wodzinski
  • , Henning Muller
  • , David G. Ellis
  • , Michele R. Aizenberg
  • M. Arda Aydin, Elvin Abdinli, Gozde Unal, Nazanin Tahmasebi, Kumaradevan Punithakumar, Tian Song, Yun Peng, Sara V. Bates, Randy Hirschtick, P. Ellen Grant, Yangming Ou*
*Corresponding author for this work
  • Boston Children's Hospital
  • Harvard University
  • University of Missouri
  • University of Toronto
  • University of Massachusetts
  • University of Pennsylvania
  • University of Applied Sciences Western Switzerland
  • AGH University of Krakow
  • University of Geneva
  • University of Nebraska Medical Center
  • Istanbul Technical University
  • University of Alberta
  • C&TS Philips
  • Nanchang University
  • Cihuai Cardio Cerebrovascular Hospital
  • Athinoula A. Martinos Center for Biomedical Imaging

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Hypoxic Ischemic Encephalopathy (HIE) represents a brain dysfunction, affecting approximately 1 to 5 per 1000 full-term neonates. The precise delineation and segmentation of HIE-related lesions in neonatal brain Magnetic Resonance Images (MRI) are pivotal in advancing outcome predictions, identifying patients at high risk, elucidating neurological manifestations, and assessing treatment efficacies. Despite its importance, the development of algorithms for segmenting HIE lesions from MRI volumes has been impeded by data scarcity. Addressing this critical gap, we organized the first BONBID-HIE challenge with diffusion MRI data (Apparent Diffusion Coefficient (ADC) maps) for HIE lesion segmentation, in conjunction with the MICCAI 2023. Totally 14 algorithms were submitted, employing a gamut of cutting-edge automatic machine-learning-based segmentation algorithms. Our comprehensive analysis of HIE lesion segmentation and submitted algorithms facilitates an in-depth evaluation of the current technological zenith, outlines directions for future advancements, and highlights persistent hurdles. To foster ongoing research and benchmarking, the annotated HIE dataset, developed algorithm dockers, and unified evaluation codes are accessible through a dedicated online platform (https://bonbid-hie2023.grand-challenge.org).

Original languageEnglish
Pages (from-to)1711-1725
Number of pages15
JournalIEEE Transactions on Medical Imaging
Volume45
Issue number4
DOIs
Publication statusPublished - Apr 2026

Bibliographical note

Publisher Copyright:
© 1982-2012 IEEE.

Keywords

  • Brain injury
  • MRI
  • algorithm comparison
  • algorithm development
  • benchmark
  • challenge
  • hypoxic ischemic encephalopathy
  • lesion segmentation
  • machine learning

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