A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network

A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network

Background: Accurate assessment of fetal descent by monitoring the fetal head (FH) station remains a clinical challenge in guiding obstetric management. Angle of progression (AoP) has been suggested to be a reliable and reproducible parameter for the assessment of FH descent.Methods: A novel framewo...

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Bibliographic Details
Main Authors: Jieyun Bai, Zhanhang Sun, Sheng Yu, Yaosheng Lu, Shun Long, Huijin Wang, Ruiyu Qiu, Zhanhong Ou, Minghong Zhou, Dengjiang Zhi, Mengqiang Zhou, Xiaosong Jiang, Gaowen Chen
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Physiology
Subjects:
angle of progression
transperineal ultrasound image
pubic symphysis
fetal head
image segmentation
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/fphys.2022.940150/full

Internet

https://www.frontiersin.org/articles/10.3389/fphys.2022.940150/full

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