Automatic Segmentation of Uterine Cavity and Placenta on MR Images Using Deep Learning

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

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor S. Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510649477
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12036
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • deep learning
  • image segmentation
  • magnetic resonance imaging (MRI)
  • Placenta
  • uterine cavity
  • uterus

ASJC Scopus subject areas

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

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