Domain generalization in fetal brain MRI segmentation \\with multi-reconstruction augmentation
This work addresses the challenge of domain generalization in fetal brain MRI segmentation for medical imaging researchers, but it is incremental as it builds on existing super-resolution techniques.
The paper tackled the problem of limited annotated data and variability in fetal brain MRI segmentation by using super-resolution reconstruction methods to generate multiple reconstructions as a data augmentation strategy, which significantly improved segmentation generalization over super-resolution pipelines.
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.