Fetal brain MR image segmentation is necessary for brain development research. Currently, this task mainly relies on labor-intensive manually contouring or correction, because automatic segmentation often fails due to the low image quality. In this work, we apply a convolutional neural network, 3D U-Net, to segment the fetal brain regions. The proposed method was validated on 209 fetal brain MRI scans, including healthy fetal controls and high-risk fetuses with congenital heart disease. The proposed method showed high consistency with the manual correction results and may facilitate the identification of aberrant fetal brain development by providing quantitative morphological information.