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Scientific article
English

Automatic fetal biometry prediction using a novel deep convolutional network architecture

Published inPhysica Medica, vol. 88, p. 127-137
Publication date2021
Abstract

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.

Keywords
  • Convolutional neural network
  • Deep learning
  • Fetal biometry
  • Image classification
  • Ultrasound imaging
Citation (ISO format)
GHELICH OGHLI, Mostafa et al. Automatic fetal biometry prediction using a novel deep convolutional network architecture. In: Physica Medica, 2021, vol. 88, p. 127–137. doi: 10.1016/j.ejmp.2021.06.020
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Identifiers
ISSN of the journal1120-1797
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Technical informations

Creation07/16/2021 1:05:00 PM
First validation07/16/2021 1:05:00 PM
Update time03/16/2023 1:26:50 AM
Status update03/16/2023 1:26:48 AM
Last indexation05/06/2024 8:16:13 AM
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