%Aigaion2 BibTeX export van HES SO Valais Publications
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@ARTICLE{,
    author = {M{\"{u}}ller, Henning and Wodzinski, Marek and Bao, Rina and Ellis, David G. and Aizenberg, Michele R.},
     month = dec,
     title = {BONBID-HIE 2023: Lesion Segmentation Challenge in BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy},
   journal = {Publimed},
      year = {2025},
       doi = {DOI: 10.1109/TMI.2025.3638977},
  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://bonbidhie2023.grand-challenge.or}
}