Payette Kelly, Steger Celine, Licandro Roxane, Dumast Priscille de, Li Hongwei Bran, Barkovich Matthew, Li Liu, Dannecker Maik, Chen Chen, Ouyang Cheng, McConnell Niccolo, Miron Alina, Li Yongmin, Uus Alena, Grigorescu Irina, Gilliland Paula Ramirez, Siddiquee Md Mahfuzur Rahman, Xu Daguang, Myronenko Andriy, Wang Haoyu, Huang Ziyan, Ye Jin, Alenya Mireia, Comte Valentin, Camara Oscar, Masson Jean-Baptiste, Nilsson Astrid, Godard Charlotte, Mazher Moona, Qayyum Abdul, Gao Yibo, Zhou Hangqi, Gao Shangqi, Fu Jia, Dong Guiming, Wang Guotai, Rieu ZunHyan, Yang HyeonSik, Lee Minwoo, Plotka Szymon, Grzeszczyk Michal K, Sitek Arkadiusz, Daza Luisa Vargas, Usma Santiago, Arbelaez Pablo, Lu Wenying, Zhang Wenhao, Liang Jing, Valabregue Romain, Joshi Anand A, Nayak Krishna N, Leahy Richard M, Wilhelmi Luca, Dandliker Aline, Ji Hui, Gennari Antonio G, Jakovcic Anton, Klaic Melita, Adzic Ana, Markovic Pavel, Grabaric Gracia, Kasprian Gregor, Dovjak Gregor, Rados Milan, Vasung Lana, Cuadra Meritxell Bach, Jakab Andras
IEEE Trans Med Imaging. 2025 Mar;44(3):1257-1272. doi: 10.1109/TMI.2024.3485554. Epub 2025 Mar 17.
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 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.
分割是分析发育中的人类胎儿大脑的关键步骤。在过去几年中，自动分割方法有了巨大改进，2021年胎儿脑组织标注（FeTA）挑战赛有助于建立出色的胎儿脑部分割标准。然而，FeTA 2021是一项单中心研究，限制了其在现实世界中的临床适用性和接受度。多中心的FeTA挑战赛2022专注于提高用于磁共振成像（MRI）的胎儿脑部分割算法的通用性。在FeTA 2022中，训练数据集包含来自两个成像中心的图像及相应的手动标注多类别标签，测试数据包含来自这两个中心以及另外两个未见过的中心的图像。多中心数据包括不同的磁共振扫描仪、成像参数以及应用的胎儿脑超分辨率算法。16个团队参与，评估了17种算法。在此，展示了挑战赛结果，重点是提交结果的通用性。在域内和域外，白质和脑室的分割准确率最高（最高Dice分数分别为0.89、0.87），而由于解剖结构复杂，最具挑战性的结构仍然是灰质（最高Dice分数为0.75）。前5名的平均Dice分数在0.81 - 0.82之间，前5名的平均百分位数豪斯多夫距离值在2.3 - 2.5毫米之间，前5名的体积相似性分数在0.90 - 0.92之间。FeTA挑战赛2022能够成功评估并提高用于MRI的多类别胎儿脑组织分割算法的通用性，并且它继续为新算法提供基准。
