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Automatic linear measurements of the fetal brain on MRI with deep neural networks

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are cerebral biparietal diameter (CBD), bone biparietal diameter (BBD), and trans-cerebellum diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.

Methods

The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: (1) computation of a region of interest that includes the fetal brain with an anisotropic 3D U-Net classifier; (2) reference slice selection with a convolutional neural network; (3) slice-wise fetal brain structures segmentation with a multi-class U-Net classifier; (4) computation of the fetal brain midsagittal line and fetal brain orientation, and; (5) computation of the measurements.

Results

Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean \(L_{1}\) difference of 1.55 mm, 1.45 mm and 1.23 mm, respectively, and a Bland–Altman 95% confidence interval (\(CI_{{95}} )\) of 3.92 mm, 3.98 mm and 2.25 mm, respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.

Conclusions

The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human-level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

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Acknowledgements

This research was supported by Kamin grant 63418 from the Israel Innovation Authority; Joseph Bar Nathan Trustee of Mychor Trust Fund. We are grateful to Vicki Myers for editorial assistance, and MRI technicians for scanning the fetuses.

Funding

This research was supported by Kamin grant 63418 from the Israel Innovation Authority; Joseph Bar Nathan Trustee of Mychor Trust Fund.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel

    Netanell Avisdris & Leo Joskowicz

  2. Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel

    Netanell Avisdris, Bossmat Yehuda, Ori Ben-Zvi, Daphna Link-Sourani & Dafna Ben Bashat

  3. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv-Yafo, Israel

    Bossmat Yehuda, Ori Ben-Zvi, Liat Ben-Sira & Dafna Ben Bashat

  4. Division of Pediatric Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv-Yafo, Israel

    Liat Ben-Sira

  5. Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv-Yafo, Israel

    Liat Ben-Sira & Dafna Ben Bashat

  6. Medical Imaging, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, Canada

    Elka Miller

  7. Radiology, Shaare Zedek Medical Center, Jerusalem, Israel

    Elena Zharkov

Authors
  1. Netanell Avisdris
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  2. Bossmat Yehuda
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Correspondence to Netanell Avisdris.

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Human rights statement

The included human study has been approved by the Tel Aviv Sourasky Medical Center institutional review board # 02–001 and performed in accordance with ethical standards.

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an earlier, partial version of this research was presented as an abstract at CARS 2020.

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Avisdris, N., Yehuda, B., Ben-Zvi, O. et al. Automatic linear measurements of the fetal brain on MRI with deep neural networks. Int J CARS 16, 1481–1492 (2021). https://doi.org/10.1007/s11548-021-02436-8

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  • DOI: https://doi.org/10.1007/s11548-021-02436-8

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