Manual fetal brain tissue segmentation is needed for training machine learning methods but is a tedious and error-prone task. The generation of synthetic magnetic resonance images can overcome the lack of clinical annotations by supplementing scarce clinical fetal datasets. However, we highlight that the choice of the numerical model from which additional data are derived is key to maximize the segmentation accuracy of clinical data via domain adaptation strategies. We demonstrate that the resort to high-resolution segmented images from real neurotypical and pathological cases enhances the morphological variability compared to an atlas, resulting in improved fetal brain tissue delineation overall.
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