Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI.

Journal: Journal of magnetic resonance imaging : JMRI
PMID: 37021577

Abstract

BACKGROUND: Automated segmentation of the placenta by MRI in early pregnancy may help predict normal and aberrant placenta function, which could improve the efficiency of placental assessment and the prediction of pregnancy outcomes. An automated segmentation method that works at one gestational age may not transfer effectively to other gestational ages.

Authors

  • Yongkai Liu
    Department of Radiological Sciences, University of California, Los Angeles, California, USA.
  • Fatemeh Zabihollahy
    Department of Systems and Computer Engineering, Carleton University, 339 Riversedge Crescent, Ottawa, ON, K1V 0Y6, Canada. fatemehzabihollahy@cmail.carleton.ca.
  • Ran Yan
    Department of Radiological Sciences, University of California, Los Angeles, California, USA.
  • Brian Lee
  • Carla Janzen
    Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
  • Sherin U Devaskar
    Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
  • Kyunghyun Sung
    Department of Radiological Sciences, University of California Los Angeles, Los Angeles, California.

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