In Proceedings of SPIE--the International Society for Optical Engineering
Magnetic resonance imaging (MRI) is useful for the detection of abnormalities affecting maternal and fetal health. In this study, we used a fully convolutional neural network for simultaneous segmentation of the uterine cavity and placenta on MR images. We trained the network with MR images of 181 patients, with 157 for training and 24 for validation. The segmentation performance of the algorithm was evaluated using MR images of 60 additional patients that were not involved in training. The average Dice similarity coefficients achieved for the uterine cavity and placenta were 92% and 80%, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of less than 1.1% compared to manual estimations. Automated segmentation, when incorporated into clinical use, has the potential to quantify, standardize, and improve placental assessment, resulting in improved outcomes for mothers and fetuses.
Shahedi Maysam, Dormer James D, Do Quyen N, Xi Yin, Lewis Matthew A, Herrera Christina L, Spong Catherine Y, Madhuranthakam Ananth J, Twickler Diane M, Fei Baowei
2022
Placenta, deep learning, image segmentation, magnetic resonance imaging (MRI), uterine cavity, uterus
