Advances in automated fetal brain MRI segmentation and biometry: Insights from the FeTA 2024 challenge.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Zalevskyi Vladyslav, Sanchez Thomas, Kaandorp Misha, Roulet Margaux, Fajardo-Rojas Diego, Li Liu, Hutter Jana, Li Hongwei Bran, Barkovich Matthew J, Ji Hui, Wilhelmi Luca, Dändliker Aline, Steger Céline, Koob Mériam, Gomez Yvan, Jakovčić Anton, Klaić Melita, Adžić Ana, Marković Pavel, Grabarić Gracia, Rados Milan, Aviles Verdera Jordina, Kasprian Gregor, Dovjak Gregor, Gaubert-Rachmühl Raphael, Aschwanden Maurice, Zeng Qi, Karimi Davood, Peruzzo Denis, Ciceri Tommaso, Longari Giorgio, Hamadache Rachika E, Bouzid Amina, Lladó Xavier, Chiarella Simone, Martí-Juan Gerard, González Ballester Miguel Ángel, Castellaro Marco, Pinamonti Marco, Visani Valentina, Cremese Robin, Sam Keïn, Gaudfernau Fleur, Ahir Param, Parikh Mehul, Zenk Maximilian, Baumgartner Michael, Maier-Hein Klaus, Tianhong Li, Hong Yang, Longfei Zhao, Preloznik Domen, Špiclin Žiga, Won Choi Jae, Li Muyang, Fu Jia, Wang Guotai, Jiang Jingwen, Tong Lyuyang, Du Bo, Gondova Andrea, You Sungmin, Im Kiho, Qayyum Abdul, Mazher Moona, Niederer Steven A, Jakab Andras, Licandro Roxane, Payette Kelly, Bach Cuadra Meritxell
Аннотация
Accurate fetal brain tissue segmentation and biometric measurement are essential for monitoring neurodevelopment and detecting abnormalities in utero. The Fetal Tissue Annotation (FeTA) Challenges have established robust multi-center benchmarks for evaluating state-of-the-art segmentation methods. This paper presents the results of the 2024 challenge edition, which introduced three key innovations. First, we introduced a topology-aware metric based on the Euler characteristic difference (ED) to overcome the performance plateau observed with traditional metrics like Dice or Hausdorff distance (HD), as the performance of the best models in segmentation surpassed the inter-rater variability. While the best teams reached similar scores in Dice (0.81-0.82) and HD95 (2.1-2.3 mm), ED provided greater discriminative power: the winning method achieved an ED of 20.9, representing roughly a 50% improvement over the second- and third-ranked teams despite comparable Dice scores. Second, we introduced a new 0.55T low-field MRI test set, which, when paired with high-quality super-resolution reconstruction, achieved the highest segmentation performance across all test cohorts (Dice=0.86, HD95=1.69, ED=6.26). This provides the first quantitative evidence that low-cost, low-field MRI can match or surpass high-field systems in automated fetal brain segmentation. Third, the new biometry estimation task exposed a clear performance gap: although the best model reached a mean average percentage error (MAPE) of 7.72%, most submissions failed to outperform a simple gestational-age-based linear regression model (MAPE=9.56%), and all remained above inter-rater variability with a MAPE of 5.38%. Finally, by analyzing the top-performing models from FeTA 2024 alongside those from previous challenge editions, we identify ensembles of 3D nnU-Net trained on both real and synthetic data with both image- and anatomy-level augmentations as the most effective approaches for fetal brain segmentation. Our quantitative analysis reveals that acquisition site, super-resolution strategy, and image quality are the primary sources of domain shift, informing recommendations to enhance the robustness and generalizability of automated fetal brain analysis methods.