Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results
arxiv(2024)
摘要
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.
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