Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. First, a super-resolution reconstruction of the uterus is generated by combining axial, sagittal and coronal views. The placenta is then segmented using 3D Gabor filters, texture features and Support Vector Machines. A uterus edge-based instance selection is proposed to identify the support vectors defining the placenta boundary. Subsequently, peripheral blood vessels are extracted through a curvature-based corner detector. Our approach is validated on a rich set of 44 control and pathological cases: singleton and (normal / monochorionic) twin pregnancies between 25–37 weeks of gestation. Dice coefficients of 0.82  ±  0.02 and 0.81  ±  0.08 are achieved for placenta and its vasculature segmentation, respectively. A comparative analysis with state of the art convolutional neural networks (CNN), namely, 3D U-Net, V-Net, DeepMedic, Holistic3D Net, HighRes3D Net and Dense V-Net is also conducted for placenta localization, with our method outperforming all CNN approaches. Results suggest that our methodology can aid the diagnosis and surgical planning of severe fetal disorders.

This work was supported by CELLEX Foundation and the Google Women Techmakers scholarship awarded to Jordina Torrents-Barrena. Also this work was funded by the Spanish Ministry of Economy and Competitiveness under the María de Maeztu Units of Excellence Programme [MDM-2015-0502]. Authors thank the GPU hardware donated by NVIDIA.