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Proceedings Paper

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

Paper Details

Date Published: 16 March 2020
PDF: 8 pages
Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, 113141R (16 March 2020); doi: 10.1117/12.2549873
Show Author Affiliations
Maysam Shahedi, The Univ. of Texas at Dallas (United States)
James D. Dormer, The Univ. of Texas at Dallas (United States)
Anusha Devi T. T. , The Univ. of Texas at Dallas (United States)
Quyen N. Do, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Yin Xi, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Matthew A. Lewis, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Ananth J. Madhuranthakam, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Diane M. Twickler, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Baowei Fei, The Univ. of Texas at Dallas (United States)
The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 11314:
Medical Imaging 2020: Computer-Aided Diagnosis
Horst K. Hahn; Maciej A. Mazurowski, Editor(s)

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