Multitask Deep Neural Network for the Fully Automatic Measurement of the Angle of Progression
Open accessAbstract
The angle of progression (AoP) for assessing fetal head (FH) descent during labor is measured from the standard plane of transperineal ultrasound images as the angle between a line through the long axis of pubic symphysis (PS) and a second line from the right end of PS tangentially to the contour of the FH. This paper presents a multitask network with a shared feature encoder and three task-special decoders for standard plane recognition (Task1), image segmentation (Task2) of PS and FH, and endpoint detection (Task3) of PS. Based on the segmented FH and two endpoints of PS from standard plane images, we determined the right FH tangent point that passes through the right endpoint of PS and then computed the AoP using the above three points. In this paper, the efficient channel attention unit is introduced into the shared feature encoder for improving the robustness of layer region encoding, while an attention fusion module is used to promote cross-branch interaction between the encoder for Task2 and that for Task3, and a shape-constrained loss function is designed for enhancing the robustness to noise based on the convex shape-prior. We use Pearson’s correlation coefficient and the Bland–Altman graph to assess the degree of agreement. The dataset includes 1964 images, where 919 images are nonstandard planes, and the other 1045 images are standard planes including PS and FH. We achieve a classification accuracy of 92.26%, and for the AoP calculation, an absolute mean (STD) value of the difference in AoP ( ∆ AoP) is 3.898° (3.192°), the Pearson’s correlation coefficient between manual and automated AoP was 0.964 and the Bland-Altman plot demonstrates they were statistically significant ( P < 0.05 ). In conclusion, our approach can achieve a fully automatic measurement of AoP with good efficiency and may help labor progress in the future.
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Lu et al. and Bai et al. proposed a method for the automated measurement of the angle of progression, which allowed for the estimation of fetal head descent by identifying the symphysis and fetal head contour.
“In addition to this, Lu et al. and Bai et al. proposed a method for the automated measurement of the angle of progression, which allowed for the estimation of fetal head descent by identifying the symphysis and fetal head contour [173,174].”
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- [52]Liu Z. Mao H. Wu C. Y. Feichtenhofer C. Darrell T. Xie S. A convnet for the 2020s In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022 11976 11986
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Jost, E., Kosian, P., Jimenez Cruz, J., Albarqouni, S., Gembruch, U., Strizek, B., & Recker, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12216833 · Cited by 44 · see citing works
“In addition to this, Lu et al. and Bai et al. proposed a method for the automated measurement of the angle of progression, which allowed for the estimation of fetal head descent by identifying the symphysis and fetal head contour [173,174].”
MethodBai, J., Sun, Z., Yu, S., Lu, Y., Long, S., Wang, H., Qiu, R., Ou, Z., Zhou, M., Zhi, D., et al. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.940150 · Cited by 36 · see citing works
Self-citation
- Authors
- Lu, Y., Zhi, D., Zhou, M., Lai, F., Chen, G., Ou, Z., Zeng, R., Long, S., Qiu, R., Zhou, M., Jiang, X., Wang, H., & Bai, J.
- Affiliations
- Jinan University (CN)
- University of Electronic Science and Technology of China (CN)
- Zhujiang Hospital (CN)
- College of Information Science and Technology, Jinan University, Guangzhou, Guangdong, China
- Corresponding author
- Bai, Jieyun
- ORCID
- Venue
- Computational and Mathematical Methods in Medicine
- Publisher
- Wiley
- Published
- 2022-09-02
- Volume
- 2022
- Pages
- 1-14
- ISSN
- 1748-670X (ISSN-L)
- 1748-6718
- PMC
- PMC9462992
- OpenAlex
- W4294295589
- Cited by
- 30
- References
- 54
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 10 · 2023: 2 · 2022: 2
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational and Mathematical Methods in Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Chengdu Science and Technology Bureau (2022-YF05-01326-SN, 2019YFC0121907, 2019YFC0120102, 2021B1212040007, 202201010544, 61901192)
- National Basic Research Program of China (2022-YF05-01326-SN, 2019YFC0121907, 2019YFC0120102, 2021B1212040007, 202201010544, 61901192)
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization (2022-YF05-01326-SN, 2019YFC0121907, 2019YFC0120102, 2021B1212040007, 202201010544, 61901192)
- Guangzhou Science and Technology Program key projects (2022-YF05-01326-SN, 2019YFC0121907, 2019YFC0120102, 2021B1212040007, 202201010544, 61901192)
- National Natural Science Foundation of China (2022-YF05-01326-SN, 2019YFC0121907, 2019YFC0120102, 2021B1212040007, 202201010544, 61901192)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- angle of progression, fetal head descent, labor progress, transperineal ultrasound, fetal head segmentation, endpoint detection, efficient channel attention, attention fusion, noise robustness, automated measurement
- MeSH terms
- Female, Fetus, Humans, Labor Presentation, Pregnancy, Reproducibility of Results, Ultrasonography, Prenatal, Neural Networks, Computer
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