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논문

Machine-learning-based automatic identification of fetal abdominal circumference from ultrasound images

등록일자 :

https://doi.org/10.1088/1361-6579/aae255

  • 저자Bukweon Kim,Ja-Young Kwon,Jin Keun Seo,Kang Cheol Kim,Yejin Park,장재성
  • 학술지Physiological Measurement (0967-3334), 39, 105007-1 ~ 105007-15
  • 등재유형SCI
  • 게재일자 20181022
Objective: Obstetricians mainly use ultrasound imaging for fetal biometric measurements. However, such measurements are cumbersome. Hence, there is urgent need for automatic biometric estimation. Automated analysis of ultrasound images is complicated owing to the patient-specific, operator-dependent, and machine-specific characteristics of such images. Approach: This paper proposes a method for the automatic fetal biometry estimation from 2D ultrasound data through several processes consisting of a specially designed convolutional neural network (CNN) and U-Net for each process.

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