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Automatic fetal biometry prediction using a novel deep convolutional network architecture

  • Author Footnotes
    1 Authors contributed equally to this manuscript.
    Mostafa Ghelich Oghli
    Correspondence
    Corresponding authors at: 10th St. Shahid Babaee Blvd. Payam Special Economic Zone, Karaj, Iran.
    Footnotes
    1 Authors contributed equally to this manuscript.
    Affiliations
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran

    Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium
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  • Ali Shabanzadeh
    Correspondence
    Corresponding authors at: 10th St. Shahid Babaee Blvd. Payam Special Economic Zone, Karaj, Iran.
    Affiliations
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran
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  • Author Footnotes
    1 Authors contributed equally to this manuscript.
    Shakiba Moradi
    Footnotes
    1 Authors contributed equally to this manuscript.
    Affiliations
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran
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  • Author Footnotes
    1 Authors contributed equally to this manuscript.
    Nasim Sirjani
    Footnotes
    1 Authors contributed equally to this manuscript.
    Affiliations
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran
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  • Reza Gerami
    Affiliations
    Radiation Sciences Research Center (RSRC), Aja University of Medical Sciences, Tehran, Iran
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  • Payam Ghaderi
    Affiliations
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran
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  • Morteza Sanei Taheri
    Affiliations
    R Department of Radiology, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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  • Isaac Shiri
    Affiliations
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
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  • Hossein Arabi
    Affiliations
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
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  • Habib Zaidi
    Affiliations
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland

    Geneva University Neurocenter, Geneva University, CH-1205 Geneva, Switzerland

    Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

    Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark
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  • Author Footnotes
    1 Authors contributed equally to this manuscript.

      Highlights

      • A convolutional neural network is proposed for fetal biometry measurement.
      • MFP-Unet architecture is modified and attention gates are incorporated.
      • A novel algorithm is also developed for detection femur images.
      • A system is proposed for determining the second channel of the network.
      • The performance of the model is evaluated using public and prepared datasets.

      Abstract

      Purpose

      Fetal biometric measurements face a number of challenges, including the presence of speckle, limited soft-tissue contrast and difficulties in the presence of low amniotic fluid. This work proposes a convolutional neural network for automatic segmentation and measurement of fetal biometric parameters, including biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), and femur length (FL) from ultrasound images that relies on the attention gates incorporated into the multi-feature pyramid Unet (MFP-Unet) network.

      Methods

      The proposed approach, referred to as Attention MFP-Unet, learns to extract/detect salient regions automatically to be treated as the object of interest via the attention gates. After determining the type of anatomical structure in the image using a convolutional neural network, Niblack's thresholding technique was applied as pre-processing algorithm for head and abdomen identification, whereas a novel algorithm was used for femur extraction. A publicly-available dataset (HC18 grand-challenge) and clinical data of 1334 subjects were utilized for training and evaluation of the Attention MFP-Unet algorithm.

      Results

      Dice similarity coefficient (DSC), hausdorff distance (HD), percentage of good contours, the conformity coefficient, and average perpendicular distance (APD) were employed for quantitative evaluation of fetal anatomy segmentation. In addition, correlation analysis, good contours, and conformity were employed to evaluate the accuracy of the biometry predictions. Attention MFP-Unet achieved 0.98, 1.14 mm, 100%, 0.95, and 0.2 mm for DSC, HD, good contours, conformity, and APD, respectively.

      Conclusions

      Quantitative evaluation demonstrated the superior performance of the Attention MFP-Unet compared to state-of-the-art approaches commonly employed for automatic measurement of fetal biometric parameters.

      Keywords

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