Improving cross-domain brain tissue segmentation in fetal MRI with
  synthetic data
Vladyslav Zalevskyi Thomas Sanchez Margaux Roulet Jordina Aviles Verddera Jana Hutter Hamza Kebiri Meritxell Bach Cuadra
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.