Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge

• URL: http://arxiv.org/abs/2505.02784v3
• Date: Thu, 08 May 2025 09:03:34 GMT
• Title: Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
• Authors: Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp, Margaux Roulet, Diego Fajardo-Rojas, Liu Li, Jana Hutter, Hongwei Bran Li, Matthew 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,
• Abstract summary: The FeTA Challenge 2024 advanced automated fetal brain MRI analysis.<n>It introduced biometry prediction as a new task alongside tissue segmentation.<n>For the first time, our diverse multi-centric test set included data from a new low-field (0.55T) MRI dataset.
• Score: 27.07002392996198
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI analysis by introducing biometry prediction as a new task alongside tissue segmentation. For the first time, our diverse multi-centric test set included data from a new low-field (0.55T) MRI dataset. Evaluation metrics were also expanded to include the topology-specific Euler characteristic difference (ED). Sixteen teams submitted segmentation methods, most of which performed consistently across both high- and low-field scans. However, longitudinal trends indicate that segmentation accuracy may be reaching a plateau, with results now approaching inter-rater variability. The ED metric uncovered topological differences that were missed by conventional metrics, while the low-field dataset achieved the highest segmentation scores, highlighting the potential of affordable imaging systems when paired with high-quality reconstruction. Seven teams participated in the biometry task, but most methods failed to outperform a simple baseline that predicted measurements based solely on gestational age, underscoring the challenge of extracting reliable biometric estimates from image data alone. Domain shift analysis identified image quality as the most significant factor affecting model generalization, with super-resolution pipelines also playing a substantial role. Other factors, such as gestational age, pathology, and acquisition site, had smaller, though still measurable, effects. Overall, FeTA 2024 offers a comprehensive benchmark for multi-class segmentation and biometry estimation in fetal brain MRI, underscoring the need for data-centric approaches, improved topological evaluation, and greater dataset diversity to enable clinically robust and generalizable AI tools.

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