Szczegóły publikacji
Opis bibliograficzny
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 WODZIŃSKI, Henning Muller, David G. Ellis, Michele R. Aizenberg, M. Arda Aydın, Elvin Abdinli, Gozde Unal, Nazanin Tahmasebi, Kumaradevan Punithakumar, Tian Song, Yun Peng, Sara V. Bates, Randy Hirschtick, P. Ellen Grant, Yangming Ou // IEEE Transactions on Medical Imaging ; ISSN 0278-0062 . — 2026 — vol. 45 no. 4, s. 1711–1725. — Bibliogr. s. 1723–1725, Abstr. — Publikacja dostępna online od: 2025-12-11. — M. Wodziński - dod. afiliacja: University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland
Autorzy (31)
- Bao Rina
- Foster Anna N.
- Song Ya'Nan
- Vyas Rutvi
- Kesri Ankush
- Toubal Imad Eddine
- Kazemi Elham Soltani
- Rahmon Gani
- Kucukpinar Taci
- Almansour Mohamed
- Ho Mai-Lan
- Palaniappan Kannappan
- Ninalga Dean
- Koirala Chiranjeewee Prasad
- Mohapatra Sovesh
- Schlaug Gottfried
- AGHWodziński Marek
- Muller Henning
- Ellis David G.
- Aizenberg Michele R.
- Aydın M. Arda
- Abdinli Elvin
- Unal Gozde
- Tahmasebi Nazanin
- Punithakumar Kumaradevan
- Song Tian
- Peng Yun
- Bates Sara V.
- Hirschtick Randy
- Grant P. Ellen
- Ou Yangming
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 166988 |
|---|---|
| Data dodania do BaDAP | 2026-04-21 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TMI.2025.3638977 |
| Rok publikacji | 2026 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Transactions on Medical Imaging |
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).
