Segmentation of uterus and placenta in MR images using a fully convolutional neural network

Maysam Shahedi, James D. Dormer, T. T. Anusha Devi, Quyen N. Do, Yin Xi, Matthew A. Lewis, Ananth J. Madhuranthakam, Diane M. Twickler, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Segmentation of the uterine cavity and placenta in fetal magnetic resonance (MR) imaging is useful for the detection of abnormalities that affect maternal and fetal health. In this study, we used a fully convolutional neural network for 3D segmentation of the uterine cavity and placenta while a minimal operator interaction was incorporated for training and testing the network. The user interaction guided the network to localize the placenta more accurately. We trained the network with 70 training and 10 validation MRI cases and evaluated the algorithm segmentation performance using 20 cases. The average Dice similarity coefficient was 92% and 82% for the uterine cavity and placenta, respectively. The algorithm could estimate the volume of the uterine cavity and placenta with average errors of 2% and 9%, respectively. The results demonstrate that the deep learning-based segmentation and volume estimation is possible and can potentially be useful for clinical applications of human placental imaging.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • convolutional neural network
  • fetal magnetic resonance imaging.
  • image segmentation
  • placenta
  • uterus

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Shahedi, M., Dormer, J. D., Anusha Devi, T. T., Do, Q. N., Xi, Y., Lewis, M. A., Madhuranthakam, A. J., Twickler, D. M., & Fei, B. (2020). Segmentation of uterus and placenta in MR images using a fully convolutional neural network. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113141R] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2549873