bibtext: @InProceedings{ Zal_Improving_MICCAI2024,
                   author = { Zalevskyi, Vladyslav and Sanchez, Thomas and Roulet, Margaux and Aviles Verdera, Jordina and Hutter, Jana and Kebiri, Hamza and Bach Cuadra, Meritxell },
                   title = { { Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15001 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 super-resolution 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-of-domain settings, validated on a 120-subject cross-domain dataset. Furthermore, we extend our evaluation to 40 subjects acquired using low-field (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.
title: Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data
authors: Zalevskyi, Vladyslav Sanchez, Thomas Roulet, Margaux Aviles Verdera, Jordina Hutter, Jana Kebiri, Hamza Bach Cuadra, Meritxell