Fetal body parts segmentation can be useful to detect abnormalities and assess fetal growth from magnetic resonance imaging (MRI). In this work, 3D fetal head, trunk and limbs body parts segmentation is leveraged for the first time by volumetric reconstructions of the whole fetal anatomy coupled to deep learning techniques for volumetric segmentation. Due to the time consuming manual segmentation required for training in this volumetric multi-label setting, sparse annotations are used, marking slices with a \(6\,\text {mm}\) separation and alternating the slicing axis between cases. These manual segmentations are used to train and compare different models using both the original MRI data and the data after volumetric reconstruction in a dataset of 45 cases. The setup consisting on 3D volumetric reconstructions and a 3D U-net based learning model results in optimal segmentation metrics, with Dice scores higher than 0.9 for all the considered structures and 0.973 for the fetal body (0.979 when highly motion corrupted datasets are discarded). In comparison, the performance of the setups that use the original MRI data exhibit a pronounced decline in segmentation scores, highlighting the importance of robust reconstruction techniques for automatic fetal growth characterization. Finally, we conduct a Bland-Altman analysis studying the reliability of our proposed 3D reconstruction and segmentation pipeline for automatic estimation of fetal body part weights.