Evidence map›Paper›PMID 41379890›Full record

ArticleIEEE transactions on medical imaging2026

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 and 21 more

Abstract read
In one paragraph

Article in IEEE transactions on medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

31 authors.

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

Funding

Consortium Of MRI Biomarkers In Neonatal Encephalopathy (COMBINE)R61NS126792 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI COTTEN, CHARLES MICHAEL, GRANT, PATRICIA ELLEN · 2022 to 2024
$2.7M
Consortium Of MRI Biomarkers In Neonatal Encephalopathy (COMBINE)R33NS126792 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI CHARLES Michael COTTEN, Patricia Ellen Grant · 2025 to 2026
$1.8M
Neural Substrate of Outcomes after Neonatal Hypoxic Ischemic EncephalopathyR21NS121735 · NINDS · BOSTON CHILDREN'S HOSPITAL · PI OU, YANGMING · 2022 to 2023
$504k
Multi-Site Clinical Data to Power MRI Biomarker of Neonatal Brain InjuryR03HD104891 · NICHD · BOSTON CHILDREN'S HOSPITAL · PI OU, YANGMING · 2021 to 2022
$190k
NICHD NIH HHS R03 HD104891NINDS NIH HHS R21 NS121735NINDS NIH HHS R33 NS126792NINDS NIH HHS R61 NS126792
6 · The paper itself

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).

Indexed as

Brain InjuriesDiffusion Magnetic Resonance ImagingHypoxia-Ischemia, BrainImage Interpretation, Computer-AssistedAlgorithmsBrainDatabases, FactualHumansInfant, NewbornMachine Learning

Identifiers

PMID41379890
PMCPMC12954739

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.