Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
Creators: name: Payette, Kelly
name: Steger, Céline
name: Licandro, Roxane
name: de Dumast, Priscille
name: Li, Hongwei Bran
name: Barkovich, Matthew
name: Li, Liu
name: Dannecker, Maik
name: Chen, Chen
name: Ouyang, Cheng
name: McConnell, Niccolò
name: Miron, Alina
name: Li, Yongmin
name: Uus, Alena
name: Grigorescu, Irina
name: Gilliland, Paula Ramirez
name: Siddiquee, Md Mahfuzur Rahman
name: Xu, Daguang
name: Myronenko, Andriy
name: Wang, Haoyu
name: Huang, Ziyan
name: Ye, Jin
name: Alenyà, Mireia
name: Comte, Valentin
name: Camara, Oscar
name: Masson, Jean-Baptiste
name: Nilsson, Astrid
name: Godard, Charlotte
name: Mazher, Moona
name: Qayyum, Abdul
name: Gao, Yibo
name: Zhou, Hangqi
name: Gao, Shangqi
name: Fu, Jia
name: Dong, Guiming
name: Wang, Guotai
name: Rieu, ZunHyan
name: Yang, HyeonSik
name: Lee, Minwoo
name: Płotka, Szymon
name: Grzeszczyk, Michal K.
name: Sitek, Arkadiusz
name: Daza, Luisa Vargas
name: Usma, Santiago
name: Arbelaez, Pablo
name: Lu, Wenying
name: Zhang, Wenhao
name: Liang, Jing
name: Valabregue, Romain
name: Joshi, Anand A.
name: Nayak, Krishna N.
name: Leahy, Richard M.
name: Wilhelmi, Luca
name: Dändliker, Aline
name: Ji, Hui
name: Gennari, Antonio G.
name: Jakovčić, Anton
name: Klaić, Melita
name: Adžić, Ana
name: Marković, Pavel
name: Grabarić, Gracia
name: Kasprian, Gregor
name: Dovjak, Gregor
name: Rados, Milan
name: Vasung, Lana
name: Cuadra, Meritxell Bach
name: Jakab, Andras
Publisher: name: arXiv
Year: 2024
Subjects: subject: Image and Video Processing (eess.IV)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: 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, and the generalizability of algorithms across different imaging centers remains unsolved, limiting real-world clinical applicability. The multi-center FeTA Challenge 2022 focuses 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 imaging centers as well as two additional unseen centers. The data from different centers varied in many aspects, including scanners used, imaging parameters, and fetal brain super-resolution algorithms applied. 16 teams participated in the challenge, and 17 algorithms were evaluated. Here, a detailed overview and analysis of the challenge results are provided, focusing on the generalizability of the submissions. Both in- and out of domain, the white matter and ventricles were segmented with the highest accuracy, while the most challenging structure remains the cerebral cortex due to anatomical complexity. 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. The resulting new methods contribute to improving the analysis of brain development in utero.
description: Results from FeTA Challenge 2022, held at MICCAI; Manuscript submitted to IEEE Transactions on Medical Imaging (2024). Supplementary Info (including submission methods descriptions) available here: https://zenodo.org/records/10628648