Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30908
Title: Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Authors: Payette, K
Steger, C
Licandro, R
De Dumast, P
Li, HB
Barkovich, M
Li, L
Dannecker, M
Chen, C
Ouyang, C
Mcconnell, N
Miron, A
Li, Y
Uus, A
Grigorescu, I
Ramirez Gilliland, P
Siddiquee, MMR
Xu, D
Myronenko, A
Wang, H
Huang, Z
Ye, J
Alenya, M
Comte, V
Camara, O
Masson, J-B
Nilsson, A
Godard, C
Mazher, M
Qayyum, A
Gao, Y
Zhou, H
Gao, S
Fu, J
Dong, G
Wang, G
Rieu, Z
Yang, H
Lee, M
Plotka, S
Grzeszczyk, MK
Sitek, A
Vargas Daza, L
Usma, S
Arbelaez, P
Lu, W
Zhang, W
Liang, J
Valabregue, R
Joshi, AA
Nayak, KN
Leahy, RM
Wilhelmi, L
Dandliker, A
Ji, H
Gennari, AG
Jakovcic, A
Klaic, M
Adzic, A
Markovic, P
Grabaric, G
Kasprian, G
Dovjak, G
Rados, M
Vasung, L
Bach Cuadra, M
Jakab, A
Keywords: deep learning;domain generalization;fetal brain MRI;multi-class image segmentation
Issue Date: 30-Oct-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Payette, K et al. (2025) 'Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results', IEEE Transactions on Medical Imaging, 44 (3), pp. 1257 - 1272. doi: 10.1109/TMI.2024.3485554.
Abstract: Segmentation is a critical step in analyzing the developing human fetal brain. There have been vast improvements in automatic segmentation methods in the past several years, and the Fetal Brain Tissue Annotation (FeTA) Challenge 2021 helped to establish an excellent standard of fetal brain segmentation. However, FeTA 2021 was a single center study, limiting real-world clinical applicability and acceptance. The multi-center FeTA Challenge 2022 focused on advancing the generalizability of fetal brain segmentation algorithms for magnetic resonance imaging (MRI). In FeTA 2022, the training dataset contained images and corresponding manually annotated multi-class labels from two imaging centers, and the testing data contained images from these two centers as well as two additional unseen centers. The multi-center data included different MR scanners, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated and 17 algorithms were evaluated. Here, the challenge results are presented, focusing on the generalizability of the submissions. Both in- and out-of-domain, the white matter and ventricles were segmented with the highest accuracy (Top Dice scores: 0.89, 0.87 respectively), while the most challenging structure remains the grey matter (Top Dice score: 0.75) due to anatomical complexity. The top 5 average Dices scores ranged from 0.81-0.82, the top 5 average 95th percentile Hausdorff distance values ranged from 2.3-2.5mm, and the top 5 volumetric similarity scores ranged from 0.90-0.92. The FeTA Challenge 2022 was able to successfully evaluate and advance generalizability of multi-class fetal brain tissue segmentation algorithms for MRI and it continues to benchmark new algorithms.
URI: https://bura.brunel.ac.uk/handle/2438/30908
DOI: https://doi.org/10.1109/TMI.2024.3485554
ISSN: 0278-0062
Other Identifiers: ORCiD: Kelly Payette https://orcid.org/0000-0001-7041-0150
ORCiD: Roxane Licandro https://orcid.org/0000-0001-9066-4473
ORCiD: Hongwei Bran Li https://orcid.org/0000-0002-5328-6407
ORCiD: Liu Li https://orcid.org/0000-0003-2376-8162
ORCiD: Maik Dannecker https://orcid.org/0000-0001-9012-9606
ORCiD: Chen Chen https://orcid.org/0000-0002-3525-9755
ORCiD: Cheng Ouyang https://orcid.org/0000-0002-3069-8708
ORCiD: Alina Miron https://orcid.org/0000-0002-0068-4495
ORCiD: Yongmin Li https://orcid.org/0000-0003-1668-2440
ORCiD: Daguang Xu https://orcid.org/0000-0002-4621-881X
ORCiD: Valentin Comte https://orcid.org/0009-0001-7512-0256
ORCiD: Oscar Camara https://orcid.org/0000-0002-5125-6132
ORCiD: Moona Mazher https://orcid.org/0000-0003-4444-5776
ORCiD: Abdul Qayyum https://orcid.org/0000-0003-3102-1595
ORCiD: Shangqi Gao https://orcid.org/0000-0003-4567-1636
ORCiD: Guotai Wang https://orcid.org/0000-0002-8632-158X
ORCiD: Michal K. Grzeszczyk https://orcid.org/0000-0002-5304-1020
ORCiD: Pablo Arbelaez https://orcid.org/0000-0001-5244-2407
ORCiD: Wenhao Zhang https://orcid.org/0000-0002-8680-1743
ORCiD: Meritxell Bach Cuadra https://orcid.org/0000-0003-2730-4285
ORCiD: Andras Jakab https://orcid.org/0000-0001-6291-9889
Appears in Collections:Dept of Computer Science Research Papers

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