TY - JOUR
T1 - BONBID-HIE 2023
T2 - Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy
AU - Bao, Rina
AU - Foster, Anna N.
AU - Song, Ya'Nan
AU - Vyas, Rutvi
AU - Kesri, Ankush
AU - Eddine Toubal, Imad
AU - Soltani Kazemi, Elham
AU - Rahmon, Gani
AU - Kucukpinar, Taci
AU - Almansour, Mohamed
AU - Ho, Mai Lan
AU - Palaniappan, K.
AU - Ninalga, Dean
AU - Prasad Koirala, Chiranjeewee
AU - Mohapatra, Sovesh
AU - Schlaug, Gottfried
AU - Wodzinski, Marek
AU - Muller, Henning
AU - Ellis, David G.
AU - Aizenberg, Michele R.
AU - Arda Aydin, M.
AU - Abdinli, Elvin
AU - Unal, Gozde
AU - Tahmasebi, Nazanin
AU - Punithakumar, Kumaradevan
AU - Song, Tian
AU - Peng, Yun
AU - Bates, Sara V.
AU - Hirschtick, Randy
AU - Ellen Grant, P.
AU - Ou, Yangming
N1 - Publisher Copyright:
© 1982-2012 IEEE.
PY - 2026/4
Y1 - 2026/4
N2 - 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).
AB - 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).
KW - Brain injury
KW - MRI
KW - algorithm comparison
KW - algorithm development
KW - benchmark
KW - challenge
KW - hypoxic ischemic encephalopathy
KW - lesion segmentation
KW - machine learning
UR - https://www.scopus.com/pages/publications/105024599387
U2 - 10.1109/TMI.2025.3638977
DO - 10.1109/TMI.2025.3638977
M3 - Article
AN - SCOPUS:105024599387
SN - 0278-0062
VL - 45
SP - 1711
EP - 1725
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 4
ER -