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, Kannappan Palaniappan, Dean Ninalga, Chiranjeewee Prasad Koirala, Sovesh Mohapatra, Gottfried Schlaug, Marek Wodzinski, Henning Müller, David G. Ellis, Michele R. Aizenberg et al. (11 additional authors not shown)

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).

External IDs:dblp:journals/tmi/BaoFSVKTKRKAHPNKMSWMEAA26