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Advances in automated fetal brain MRI segmentation and biometry: Insights from the FeTA 2024 challenge

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. T...

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Autori principali: Zalevskyi, Vladyslav (Autore) , Sanchez, Thomas (Autore) , Kaandorp, Misha (Autore) , Roulet, Margaux (Autore) , Fajardo-Rojas, Diego (Autore) , Li, Liu (Autore) , Hutter, Jana (Autore) , Li, Hongwei Bran (Autore) , Barkovich, Matthew J. (Autore) , Ji, Hui (Autore) , Wilhelmi, Luca (Autore) , Dändliker, Aline (Autore) , Steger, Céline (Autore) , Koob, Mériam (Autore) , Gomez, Yvan (Autore) , Jakovčić, Anton (Autore) , Klaić, Melita (Autore) , Adžić, Ana (Autore) , Marković, Pavel (Autore) , Grabarić, Gracia (Autore) , Rados, Milan (Autore) , Aviles Verdera, Jordina (Autore) , Kasprian, Gregor (Autore) , Dovjak, Gregor (Autore) , Gaubert-Rachmühl, Raphael (Autore) , Aschwanden, Maurice (Autore) , Zeng, Qi (Autore) , Karimi, Davood (Autore) , Peruzzo, Denis (Autore) , Ciceri, Tommaso (Autore) , Longari, Giorgio (Autore) , Hamadache, Rachika E. (Autore) , Bouzid, Amina (Autore) , Lladó, Xavier (Autore) , Chiarella, Simone (Autore) , Martí-Juan, Gerard (Autore) , González Ballester, Miguel Ángel (Autore) , Castellaro, Marco (Autore) , Pinamonti, Marco (Autore) , Visani, Valentina (Autore) , Cremese, Robin (Autore) , Sam, Keïn (Autore) , Gaudfernau, Fleur (Autore) , Ahir, Param (Autore) , Parikh, Mehul (Autore) , Zenk, Maximilian (Autore) , Baumgartner, Michael (Autore) , Maier-Hein, Klaus H. (Autore) , Tianhong, Li (Autore) , Hong, Yang (Autore) , Longfei, Zhao (Autore) , Preloznik, Domen (Autore) , Špiclin, Žiga (Autore) , Won Choi, Jae (Autore) , Li, Muyang (Autore) , Fu, Jia (Autore) , Wang, Guotai (Autore) , Jiang, Jingwen (Autore) , Tong, Lyuyang (Autore) , Du, Bo (Autore) , Gondova, Andrea (Autore) , You, Sungmin (Autore) , Im, Kiho (Autore) , Qayyum, Abdul (Autore) , Mazher, Moona (Autore) , Niederer, Steven A. (Autore) , Jakab, Andras (Autore) , Licandro, Roxane (Autore) , Payette, Kelly (Autore) , Bach Cuadra, Meritxell (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: March 2026
In: Medical image analysis
Year: 2026, Volume: 109, Pages: 1-22
ISSN:1361-8423
DOI:10.1016/j.media.2026.103941
Accesso online:Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.media.2026.103941
Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S1361841526000101
Testo
Note sull'autore:Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp, Margaux Roulet, Diego Fajardo-Rojas, Liu Li, Jana Hutter, Hongwei Bran Li, Matthew J. Barkovich, Hui Ji, Luca Wilhelmi, Aline Dändliker, Céline Steger, Mériam Koob, Yvan Gomez, Anton Jakovčić, Melita Klaić, Ana Adžić, Pavel Marković, Gracia Grabarić, Milan Rados, Jordina Aviles Verdera, Gregor Kasprian, Gregor Dovjak, Raphael Gaubert-Rachmühl, Maurice Aschwanden, Qi Zeng, Davood Karimi, Denis Peruzzo, Tommaso Ciceri, Giorgio Longari, Rachika E. Hamadache, Amina Bouzid, Xavier Lladó, Simone Chiarella, Gerard Martí-Juan, Miguel Ángel González Ballester, Marco Castellaro, Marco Pinamonti, Valentina Visani, Robin Cremese, Keïn Sam, Fleur Gaudfernau, Param Ahir, Mehul Parikh, Maximilian Zenk, Michael Baumgartner, Klaus Maier-Hein, Li Tianhong, Yang Hong, Zhao Longfei, Domen Preloznik, Žiga Špiclin, Jae Won Choi, Muyang Li, Jia Fu, Guotai Wang, Jingwen Jiang, Lyuyang Tong, Bo Du, Andrea Gondova, Sungmin You, Kiho Im, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Andras Jakab, Roxane Licandro, Kelly Payette, Meritxell Bach Cuadra
Descrizione
Riassunto: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 (2.1-2.3mm), 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.
Descrizione del documento:Gesehen am 12.06.2026
Descrizione fisica:Online Resource
ISSN:1361-8423
DOI:10.1016/j.media.2026.103941

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