Fetal brain tissue segmentation from magnetic resonance imaging (MRI) is crucial for studying neurodevelopment, but remains challenging due to data heterogeneity and limited annotations. Domain randomization (DR) has recently emerged as a promising strategy for single-source domain generalization by synthesizing training images with randomized artifacts, contrast, and resolution. In this work, we investigate how to maximize the out-of-domain (OOD) generalization of DR-based methods. We evaluate several synthetic data generation strategies for DR, with a particular focus on our recently proposed framework, FetalSynthSeg. We show that simple Gaussian mixture-based intensity modeling outperforms more complex physics-based simulations, and that intensity clustering (subdividing tissue classes based on intensity) improves OOD robustness. Evaluated on 348 fetal subjects from four sites spanning 0.55-3T and both T1w and T2w contrasts, FetalSynthSeg reaches state-of-the-art performance on several FeTA 2024 testing datasets (80-85 Dice score) and, for the first time, offers robust segmentation on modalities other than T2w for fetal brain segmentation (80 Dice on dHCP-T1w dataset). Compared with state-of-the-art methods such as BOUNTI, nnU-Net ensemble, and the FeTA 2024 winner, FetalSynthSeg delivers comparable or superior accuracy while maintaining strong robustness across domain shifts. Our code, model weights, and Docker image ready for easy inference are available at https://hub.docker.com/r/vzalevskyi/fetalsynthseg.

翻译：胎儿脑组织分割对于神经发育研究至关重要，但由于数据异质性和标注有限，该任务仍具有挑战性。域随机化（DR）作为一种有前景的单源域泛化策略，通过合成带有随机伪影、对比度和分辨率的训练图像来应对此问题。本研究探究如何最大化基于DR方法的域外（OOD）泛化性能。我们评估了多种面向DR的合成数据生成策略，重点关注近期提出的FetalSynthSeg框架。结果表明，基于高斯混合模型的简单强度建模优于更复杂的物理仿真方法，而强度聚类（根据强度细分组织类别）可提升OOD鲁棒性。在来自四个站点的348个胎儿数据上（涵盖0.55-3T场强及T1w/T2w对比序列），FetalSynthSeg在多个FeTA 2024测试数据集上达到最优性能（Dice分数80-85），并首次实现了非T2w模态的稳健胎儿脑分割（dHCP-T1w数据集Dice分数80）。与BOUNTI、nnU-Net集成模型及FeTA 2024冠军方法等最新技术相比，FetalSynthSeg在保持跨域迁移鲁棒性的同时，取得了相当或更优的精度。我们的代码、模型权重及可直接推理的Docker镜像已发布于 https://hub.docker.com/r/vzalevskyi/fetalsynthseg。