Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach.

Journal: AJNR. American journal of neuroradiology
Published Date: Feb 17, 2022

Abstract

BACKGROUND AND PURPOSE: MR imaging provides critical information about fetal brain growth and development. Currently, morphologic analysis primarily relies on manual segmentation, which is time-intensive and has limited repeatability. This work aimed to develop a deep learning-based automatic fetal brain segmentation method that provides improved accuracy and robustness compared with atlas-based methods.

Authors

  • L Zhao
  • J D Asis-Cruz
    From the Department of Diagnostic Imaging and Radiology (L.Z., J.D.A.-C., Y.W., K.K., A.L., J.Q., C. Lopez, C. Limperopoulos), Developing Brain Institute, Children's National, Washington, DC.
  • X Feng
    Department of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia.
  • Y Wu
    School of Biomedical Informatics, The University of Texas Health Science Center at Houston , Houston, Texas, USA.
  • K Kapse
    From the Department of Diagnostic Imaging and Radiology (L.Z., J.D.A.-C., Y.W., K.K., A.L., J.Q., C. Lopez, C. Limperopoulos), Developing Brain Institute, Children's National, Washington, DC.
  • A Largent
    Developing Brain Institute, Department of Diagnostic Imaging and Radiology, Children's National Hospital, Washington, DC, USA.
  • J Quistorff
    From the Department of Diagnostic Imaging and Radiology (L.Z., J.D.A.-C., Y.W., K.K., A.L., J.Q., C. Lopez, C. Limperopoulos), Developing Brain Institute, Children's National, Washington, DC.
  • C Lopez
    Nuritas Ltd, Joshua Dawson House, Dawson St, Dublin 2, D02 RY95, Ireland.
  • D Wu
    Department of Biomedical Engineering (L.Z., D.W.), Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, China.
  • K Qing
    Department of Radiation Oncology (K.Q.), City of Hope National Center, Duarte, California.
  • C Meyer
    Department of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia.
  • C Limperopoulos
    From the Department of Diagnostic Imaging and Radiology (L.Z., J.D.A.-C., Y.W., K.K., A.L., J.Q., C. Lopez, C. Limperopoulos), Developing Brain Institute, Children's National, Washington, DC climpero@childrensnational.org.

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