Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

Journal: European radiology
PMID: 37658890

Abstract

OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR).

Authors

  • Bella Specktor-Fadida
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel. bella.specktor@mail.huji.ac.il.
  • Daphna Link-Sourani
    Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Aviad Rabinowich
    Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Elka Miller
  • Anna Levchakov
    Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
  • Netanell Avisdris
    School of Computer Science and Engineering, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Liat Ben-Sira
    Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, 6997801, Israel.
  • Liran Hiersch
    Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Leo Joskowicz
    School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
  • Dafna Ben-Bashat
    Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. dafnab@tlvmc.gov.il.

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