Automatic fetal biometry prediction using a novel deep convolutional network architecture.

Journal: Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Published Date: Aug 1, 2021

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.

Authors

  • Mostafa Ghelich Oghli
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran; Department of Cardiovascular Sciences, KU Leuven, Leuven, Belgium. Electronic address: m.g31_mesu@yahoo.com.
  • Ali Shabanzadeh
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran. Electronic address: shabanzadeh.ali@gmail.com.
  • Shakiba Moradi
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran; Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran.
  • Nasim Sirjani
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran.
  • Reza Gerami
    Radiation Sciences Research Center (RSRC), Aja University of Medical Sciences, Tehran, Iran.
  • Payam Ghaderi
    Research and Development Department, Med Fanavarn Plus Co., Karaj, Iran.
  • Morteza Sanei Taheri
    R Department of Radiology, Shohada-e-Tajrish Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Hossein Arabi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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