20.500.12556/RUL-178379 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. 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 superresolution 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 gestationalage-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. fetal brain magnetic resonance imaging low-field segmentation topology biometry domain shift challenge results možgani ploda magnetnoresonančno slikanje segmentacija slik pri šibkem magnetnem polju topologija biometrija premestitev domene rezultati računskega izziva true false true Angleški jezik Slovenski jezik Članek v reviji 2026-01-26 10:38:04 2026-01-26 10:38:14 2026-01-27 04:23:10 0000-00-00 00:00:00 2026 0 0 V objavljenem članku je navedenih veliko virov financiranja raziskave 22 str. art. 103941 Vol. 109 Mar. 2026 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.93:61 1361-8415 10.1016/j.media.2026.103941 265974787 1-s2.0-S1361841526000101-main.pdf 1-s2.0-S1361841526000101-main.pdf 1 EC6BA9A20910CD6BB278E9E0684C4F08 0b24dc49531c530a3c7e73dc61f3b764f49a1e35af9120e1432188ca78746235 bbe43c10-fa9c-11f0-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=226171 https://www.sciencedirect.com/science/article/pii/S1361841526000101 1 bbebdb7d-fa9a-11f0-a1ba-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=226170 Fakulteta za elektrotehniko 0 0 0