AUTHOR=Bai Jieyun , Sun Zhanhang , Yu Sheng , Lu Yaosheng , Long Shun , Wang Huijin , Qiu Ruiyu , Ou Zhanhong , Zhou Minghong , Zhi Dengjiang , Zhou Mengqiang , Jiang Xiaosong , Chen Gaowen
TITLE=A framework for computing angle of progression from transperineal ultrasound images for evaluating fetal head descent using a novel double branch network
JOURNAL=Frontiers in Physiology
VOLUME=13
YEAR=2022
URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2022.940150
DOI=10.3389/fphys.2022.940150
ISSN=1664-042X
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 input of the upper branch to assist the upper branch in decoding after being constrained by the attention gate (AG). Given an original transperineal ultrasound (TPU) image, areas of the pubic symphysis (PS) and FH are firstly segmented using the proposed DBSN, the ellipse contours of segmented regions are secondly fitted with the least square method, and three endpoints are finally determined for calculating AoP.
Results: Our private dataset with 313 transperineal ultrasound (TPU) images was used for model evaluation with 5-fold cross-validation. The proposed method achieves the highest Dice coefficient (93.4%), the smallest Average Surface Distance (6.268 pixels) and the lowest AoP difference (5.993°) by comparing four state-of-the-art methods. Similar results (Dice coefficient: 91.7%, Average Surface Distance: 7.729 pixels: AoP difference: 5.110°) were obtained on a public dataset with >3,700 TPU images for evaluating its generalization performance.
Conclusion: The proposed framework may be used for the automatic measurement of AoP with high accuracy and generalization performance. However, its clinical availability needs to be further evaluated.