The Ganglionic Eminence (GE) plays a pivotal role in neural migration during fetal brain development. Early detection of GE anomalies is crucial for identifying migration deficiencies that may lead to neurological or psychiatric disorders postnatally. Currently, no robust automatic GE segmentation approaches exist to enable its early analysis. Segmentation challenges arise from the transient nature of this structure, resulting in an increase in isolated components and a decrease in their volumes with age. Additional complexity is introduced by the inaccuracy of determining the gestational age, potential artifacts due to fetal movement, and intensity fluctuations in images for the same structure. In this work, we propose an automated GE segmentation method for fetal Magnetic Resonance Imaging (MRI) data by extending 3D UNets and exploiting a registration–driven generative data augmentation technique to increase the number of scans from 138 to more than 2,500 with manually defined labels by an expert neuroradiologist. Our solution spans 19 to 38 weeks of gestation, achieving a mean Dice score of 0.79 ± 0.04 in the 2nd trimester and 0.74 ± 0.05 in the 3rd trimester, matching current state-of-the-art models. Overall, the GE volume decreases throughout pregnancy (R2 = 0.77, ranged 31– 668 mm3), highlighting an inverse relationship to the whole brain volume, which continues to grow (R2 = 0.93). The volumetric dynamics of predicted GE segmentations correlate with known anatomical development patterns, indicating the model’s ability to learn GE dynamics over time.