Segmentation of the fetal brain into its components is important for quantitative assessment of fetal development. This study proposes a fully automatic method based on deep learning for fetal brain segmentation into six components, including a separation of right and left hemispheres. The method’s performance demonstrated high Dice scores for all brain components and robustness to different contrasts, scan resolutions, gestational age and fetal brain pathologies. Preliminary results demonstrated significant larger ventricle’s volumes and asymmetry in fetuses with ventriculomegaly compared to normal fetuses. The method is suggested to improve fetal assessment and assist radiologists in routine clinical practice.
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