Segmentation of fetal brain tissue from magnetic resonance imaging (MRI) plays a crucial role in the study of in utero neurodevelopment. However, automated tools face substantial domain shift challenges as they must be robust to highly heterogeneous clinical data, often limited in numbers and lacking annotations. Indeed, high variability of the fetal brain morphology, MRI acquisition parameters, and superresolution reconstruction (SR) algorithms adversely affect the model's performance when evaluated out-of-domain. In this work, we introduce FetalSynthSeg, a domain randomization method to segment fetal brain MRI, inspired by SynthSeg. Our results show that models trained solely on synthetic data outperform models trained on real data in out-ofdomain settings, validated on a 120-subject cross-domain dataset. Furthermore, we extend our evaluation to 40 subjects acquired using lowfield (0.55T) MRI and reconstructed with novel SR models, showcasing robustness across different magnetic field strengths and SR algorithms. Leveraging a generative synthetic approach, we tackle the domain shift problem in fetal brain MRI and offer compelling prospects for applications in fields with limited and highly heterogeneous data.

翻译：胎儿脑组织磁共振成像（MRI）分割在宫内神经发育研究中具有关键作用。然而，自动化工具面临显著的域偏移挑战，必须对高度异质性的临床数据保持鲁棒性，而这些数据往往数量有限且缺乏标注。实际上，胎儿脑形态学、MRI采集参数及超分辨率重建（SR）算法的高度变异性，会严重影响模型在域外评估时的表现。本研究提出FetalSynthSeg——一种受SynthSeg启发的域随机化方法，用于胎儿脑MRI分割。结果表明，在120名受试者的跨域数据集中，仅基于合成数据训练的模型在域外场景下表现优于真实数据训练的模型。此外，我们将评估扩展至40名采用低场（0.55T）MRI采集并通过新型SR模型重建的受试者，展示了模型在不同磁场强度和SR算法下的鲁棒性。通过利用生成式合成方法，我们解决了胎儿脑MRI中的域偏移问题，为数据有限且高度异质性领域的应用提供了令人振奋的前景。