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

ABSTRACT: 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 framework, including image segmentation, target fitting and AoP calculation, is proposed for evaluating fetal descent. For image segmentation, this study presents a novel double branch segmentation network (DBSN), which consists of two parts: an encoding part receives image input, and a decoding part composed of deformable convolutional blocks and ordinary convolutional blocks. The decoding part includes the lower and upper branches, and the feature map of the lower branch is used as the

SUBMITTER: Bai J 

PROVIDER: S-EPMC9755498 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature