@inproceedings{924e36dd815949cc9d57223e163a2328,
title = "Deep-learning-based automatic segmentation of the placenta and uterine cavity on prenatal MR images",
abstract = "Magnetic resonance imaging (MRI) has potential benefits in understanding fetal and placental complications in pregnancy. An accurate segmentation of the uterine cavity and placenta can help facilitate fast and automated analyses of placenta accreta spectrum and other pregnancy complications. In this study, we trained a deep neural network for fully automatic segmentation of the uterine cavity and placenta from MR images of pregnant women with and without placental abnormalities. The two datasets were axial MRI data of 241 pregnant women, among whom, 101 patients also had sagittal MRI data. Our trained model was able to perform fully automatic 3D segmentation of MR image volumes and achieved an average Dice similarity coefficient (DSC) of 92% for uterine cavity and of 82% for placenta on the sagittal dataset and an average DSC of 87% for uterine cavity and of 82% for placenta on the axial dataset. Use of our automatic segmentation method is the first step in designing an analytics tool for to assess the risk of pregnant women with placenta accreta spectrum.",
keywords = "Placenta, deep learning, image segmentation, magnetic resonance imaging (MRI), neural network, placenta accreta spectrum, uterus",
author = "James Huang and Do, {Quyen N.} and Maysam Shahed and Yin Xi and Lewis, {Matthew A.} and Herrera, {Christina L.} and David Owen and Spong, {Catherine Y.} and Madhuranthakam, {Ananth J.} and Twickler, {Diane M.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2023 SPIE.; Medical Imaging 2023: Computer-Aided Diagnosis ; Conference date: 19-02-2023 Through 23-02-2023",
year = "2023",
doi = "10.1117/12.2653659",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Iftekharuddin, {Khan M.} and Weijie Chen",
booktitle = "Medical Imaging 2023",
}