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Improved Turnaround Times | Median time to first decision: 12 days

Research ArticlePediatric Neuroimaging
Open Access

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

L. Zhao, J.D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer and C. Limperopoulos
American Journal of Neuroradiology March 2022, 43 (3) 448-454; DOI: https://doi.org/10.3174/ajnr.A7419
L. Zhao
aFrom 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
bDepartment 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
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  • ORCID record for L. Zhao
J.D. Asis-Cruz
aFrom 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
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X. Feng
cDepartment of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia
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  • ORCID record for X. Feng
Y. Wu
aFrom 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
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K. Kapse
aFrom 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
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A. Largent
aFrom 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
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  • ORCID record for A. Largent
J. Quistorff
aFrom 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
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C. Lopez
aFrom 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
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D. Wu
bDepartment 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
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K. Qing
dDepartment of Radiation Oncology (K.Q.), City of Hope National Center, Duarte, California
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C. Meyer
cDepartment of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia
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C. Limperopoulos
aFrom 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
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Article Figures & Data

Figures

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  • FIG 1.
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    FIG 1.

    Comparison of segmentation methods on healthy fetuses of early and late GAs.

  • FIG 2.
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    FIG 2.

    Regional comparisons between the proposed and conventional methods. The asterisk indicates P < .001. Cere indicates cerebellum.

  • FIG 3.
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    FIG 3.

    Regional performance across GAs.

  • FIG 4.
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    FIG 4.

    Brain segmentation in a fetus with CHD. A, Manually corrected segmentation. B, The proposed method.

Tables

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    Table 1:

    Average performance of 3D U-Net augmentation methods and normalization across 5 repetitions

    Flip AugmentationNo Normalization
    None3 DirectionLeft-RightLeft-Right
    Training
     Cross-entropy0.0461 (SD, 0.0017)0.0596 (SD, 0.0006)0.0502 (SD, 0.0008)0.03504 (SD, 0.0010)
     Dice score0.9380 (SD, 0.0016)0.9140 (SD, 0.0004)0.9315 (SD, 0.0012)0.9284 (SD, 0.0008)
    Validation
     Cross-entropy0.0726 (SD, 0.0041)0.07472 (SD, 0.0050)0.06771 (SD, 0.0039)0.04062 (SD, 0.0023)
     Dice score0.8698 (SD, 0.0136)0.8518 (SD, 0.0163)0.8819 (SD, 0.0122)0.8419 (SD, 0.0279)
    • View popup
    Table 2:

    Dice scores per region

    CSFCGMWMDGMCereBS
    3D U-Net0.9220.8280.9080.8840.9350.902
    Altas-based0.8080.7070.8450.7550.8670.855
    • Note:—Cere indicates cerebellum

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American Journal of Neuroradiology: 43 (3)
American Journal of Neuroradiology
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1 Mar 2022
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Cite this article
L. Zhao, J.D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer, C. Limperopoulos
Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
American Journal of Neuroradiology Mar 2022, 43 (3) 448-454; DOI: 10.3174/ajnr.A7419

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Automated 3D Fetal Brain Segmentation Using an Optimized Deep Learning Approach
L. Zhao, J.D. Asis-Cruz, X. Feng, Y. Wu, K. Kapse, A. Largent, J. Quistorff, C. Lopez, D. Wu, K. Qing, C. Meyer, C. Limperopoulos
American Journal of Neuroradiology Mar 2022, 43 (3) 448-454; DOI: 10.3174/ajnr.A7419
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