  Quantitative analysis of in utero human brain development is crucial for
abnormal characterization. Magnetic resonance image (MRI) segmentation is
therefore an asset for quantitative analysis. However, the development of
automated segmentation methods is hampered by the scarce availability of fetal
brain MRI annotated datasets and the limited variability within these cohorts.
In this context, we propose to leverage the power of fetal brain MRI
super-resolution (SR) reconstruction methods to generate multiple
reconstructions of a single subject with different parameters, thus as an
efficient tuning-free data augmentation strategy. Overall, the latter
significantly improves the generalization of segmentation methods over SR
pipelines.
