@inproceedings{5e4ced8f432e472495ab0f71ddf4aee5,
title = "Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning",
abstract = "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.",
keywords = "Placenta, deep learning, image segmentation, magnetic resonance imaging (MRI), uterine cavity, uterus",
author = "Maysam Shahedi and Dormer, {James D.} and Do, {Quyen N.} and Yin Xi and Lewis, {Matthew A.} and Herrera, {Christina L.} and Spong, {Catherine Y.} and Madhuranthakam, {Ananth J.} and Twickler, {Diane M.} and Baowei Fei",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE; Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 21-03-2022 Through 27-03-2022",
year = "2022",
doi = "10.1117/12.2613286",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Gimi, {Barjor S.} and Andrzej Krol",
booktitle = "Medical Imaging 2022",
}