Automatic segmentation of uterine cavity and placenta on MR images using deep learning

Maysam Shahedi; James D. Dormer; Quyen N. Do; Yin Xi; Matthew A. Lewis; Christina L. Herrera; Catherine Y. Spong; Ananth J. Madhuranthakam; Diane M. Twickler; Baowei Fei

doi:10.1117/12.2613286

4 April 2022 Automatic segmentation of uterine cavity and placenta on MR images using deep learning

Maysam Shahedi, James D. Dormer, Quyen N. Do, Yin Xi, Matthew A. Lewis, Christina L. Herrera, Catherine Y. Spong, Ananth J. Madhuranthakam, Diane M. Twickler, Baowei Fei

Author Affiliations +

Proceedings Volume 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1203611 (2022) https://doi.org/10.1117/12.2613286
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

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Citation Download Citation

Maysam Shahedi, James D. Dormer, Quyen N. Do, Yin Xi, Matthew A. Lewis, Christina L. Herrera, Catherine Y. Spong, Ananth J. Madhuranthakam, Diane M. Twickler, and Baowei Fei "Automatic segmentation of uterine cavity and placenta on MR images using deep learning", Proc. SPIE 12036, Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1203611 (4 April 2022); https://doi.org/10.1117/12.2613286

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