Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
This addresses the challenge of limited and heterogeneous clinical data for fetal brain MRI segmentation, offering improved robustness for neurodevelopmental studies, though it is incremental as it builds on existing domain randomization methods.
The authors tackled the problem of domain shift in automated fetal brain tissue segmentation from MRI by introducing FetalSynthSeg, a domain randomization method, and showed that models trained solely on synthetic data outperformed those trained on real data in out-of-domain settings, validated on a 120-subject cross-domain dataset and extended to 40 subjects with low-field MRI.
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.