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

doi:10.3174/ajnr.A7419

. 2022 Mar;43(3):448-454.

doi: 10.3174/ajnr.A7419. Epub 2022 Feb 17.

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

L Zhao^{1

2}, J D Asis-Cruz¹, X Feng³, Y Wu¹, K Kapse¹, A Largent¹, J Quistorff¹, C Lopez¹, D Wu², K Qing⁴, C Meyer³, C Limperopoulos⁵

Affiliations

¹ 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.
² 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.
³ Department of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia.
⁴ Department of Radiation Oncology (K.Q.), City of Hope National Center, Duarte, California.
⁵ 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.

PMID: 35177547
PMCID: PMC8910820
DOI: 10.3174/ajnr.A7419

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

L Zhao et al. AJNR Am J Neuroradiol. 2022 Mar.

. 2022 Mar;43(3):448-454.

doi: 10.3174/ajnr.A7419. Epub 2022 Feb 17.

Authors

L Zhao^{1

2}, J D Asis-Cruz¹, X Feng³, Y Wu¹, K Kapse¹, A Largent¹, J Quistorff¹, C Lopez¹, D Wu², K Qing⁴, C Meyer³, C Limperopoulos⁵

Affiliations

¹ 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.
² 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.
³ Department of Biomedical Engineering (X.F., C.M.), University of Virginia, Charlottesville, Virginia.
⁴ Department of Radiation Oncology (K.Q.), City of Hope National Center, Duarte, California.
⁵ 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.

PMID: 35177547
PMCID: PMC8910820
DOI: 10.3174/ajnr.A7419

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.

Materials and methods: A total of 106 fetal MR imaging studies were acquired prospectively from fetuses between 23 and 39 weeks of gestation. We trained a deep learning model on the MR imaging scans of 65 healthy fetuses and compared its performance with a 4D atlas-based segmentation method using the Wilcoxon signed-rank test. The trained model was also evaluated on data from 41 fetuses diagnosed with congenital heart disease.

Results: The proposed method showed high consistency with the manual segmentation, with an average Dice score of 0.897. It also demonstrated significantly improved performance (P < .001) based on the Dice score and 95% Hausdorff distance in all brain regions compared with the atlas-based method. The performance of the proposed method was consistent across gestational ages. The segmentations of the brains of fetuses with high-risk congenital heart disease were also highly consistent with the manual segmentation, though the Dice score was 7% lower than that of healthy fetuses.

Conclusions: The proposed deep learning method provides an efficient and reliable approach for fetal brain segmentation, which outperformed segmentation based on a 4D atlas and has been used in clinical and research settings.

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Figures

**FIG 1.**
Comparison of segmentation methods on healthy fetuses of early and late GAs.

**FIG 2.**
Regional comparisons between the proposed and conventional methods. The *asterisk* indicates P < .001. Cere indicates cerebellum.

**FIG 3.**
Regional performance across GAs.

**FIG 4.**
Brain segmentation in a fetus with CHD. A, Manually corrected segmentation. B, The proposed method.

See this image and copyright information in PMC

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References

1. Clouchoux C, Guizard N, Evans AC, et al. . Normative fetal brain growth by quantitative in vivo magnetic resonance imaging. Am J Obstet Gynecol 2012;206:173.e1–8 10.1016/j.ajog.2011.10.002 - DOI - PubMed
1. Limperopoulos C. Disorders of the fetal circulation and the fetal brain. Clin Perinatol 2009;36:561–77 10.1016/j.clp.2009.07.005 - DOI - PubMed
1. Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015;2015:450341 10.1155/2015/450341 - DOI - PMC - PubMed
1. Xue H, Srinivasan L, Jiang S, et al. . Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 2007;38:461–77 10.1016/j.neuroimage.2007.07.030 - DOI - PubMed
1. Sui Y, Afacan O, Gholipour A, et al. . Fast and high-resolution neonatal brain MRI through super-resolution reconstruction from acquisitions with variable slice selection direction. Front Neurosci 2021;15:636268 10.3389/fnins.2021.636268 - DOI - PMC - PubMed

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[1] Clouchoux C, Guizard N, Evans AC, et al. . Normative fetal brain growth by quantitative in vivo magnetic resonance imaging. Am J Obstet Gynecol 2012;206:173.e1–8 10.1016/j.ajog.2011.10.002 - DOI - PubMed

[2] Clouchoux C, Guizard N, Evans AC, et al. . Normative fetal brain growth by quantitative in vivo magnetic resonance imaging. Am J Obstet Gynecol 2012;206:173.e1–8 10.1016/j.ajog.2011.10.002 - DOI - PubMed

[3] Limperopoulos C. Disorders of the fetal circulation and the fetal brain. Clin Perinatol 2009;36:561–77 10.1016/j.clp.2009.07.005 - DOI - PubMed

[4] Limperopoulos C. Disorders of the fetal circulation and the fetal brain. Clin Perinatol 2009;36:561–77 10.1016/j.clp.2009.07.005 - DOI - PubMed

[5] Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015;2015:450341 10.1155/2015/450341 - DOI - PMC - PubMed

[6] Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015;2015:450341 10.1155/2015/450341 - DOI - PMC - PubMed

[7] Xue H, Srinivasan L, Jiang S, et al. . Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 2007;38:461–77 10.1016/j.neuroimage.2007.07.030 - DOI - PubMed

[8] Xue H, Srinivasan L, Jiang S, et al. . Automatic segmentation and reconstruction of the cortex from neonatal MRI. Neuroimage 2007;38:461–77 10.1016/j.neuroimage.2007.07.030 - DOI - PubMed

[9] Sui Y, Afacan O, Gholipour A, et al. . Fast and high-resolution neonatal brain MRI through super-resolution reconstruction from acquisitions with variable slice selection direction. Front Neurosci 2021;15:636268 10.3389/fnins.2021.636268 - DOI - PMC - PubMed

[10] Sui Y, Afacan O, Gholipour A, et al. . Fast and high-resolution neonatal brain MRI through super-resolution reconstruction from acquisitions with variable slice selection direction. Front Neurosci 2021;15:636268 10.3389/fnins.2021.636268 - DOI - PMC - PubMed

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

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

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