Intrapartum ultrasound image analysis

An automatic measurement of cervix dilation in intrapartum ultrasound image
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Accurate segmentation of cervix in intrapartum ultrasound images and measurement of cervical dilation (CD) are crucial for monitoring labor progress and predicting delivery outcomes. However, the poor quality of ultrasound images like artifacts and missing boundaries makes the measurement inaccurate and time- and effort-consuming. This paper proposes CDNet, a network for automatic measurement of CD. First, CDNet accurately segments the cervix mask, and then the center of the mask is located to find the horizontal and vertical intersection points to calculate the transverse diameter and anteroposterior diameter of CD. A shape-constraint loss function is used to improve segmentation accuracy by the prior convex shape. Experiments show that CDNet achieved the lowest Relative Volume Error of 15.56 ± 1.80%, the highest Dice of 89.55 ± 0.47%, and the lowest 95% Hausdorff distance of 11.48 ± 0.63 mm. The measurement of transverse diameter and anteroposterior diameter based on CDNet achieved a mean absolute error of 1.79 ± 0.24 mm and 2.67 ± 0.22 mm respectively. Moreover, CDNet also outperformed its counterparts in further experiments on two additional fetal head datasets. In conclusion, our method can achieve automatic CD measurement with good performance and may help assess labor progress in the future.

  1. Bai, J., Yang, Z., Hasan, K., Gan, J., Liang, Z., Cai, W., Tan, T., Ye, J., Yaqub, M., Ni, D., Slimani, S., Ohene-Botwe, B., Roman Victor Manuel, C., & Lekadir, K. (2024). Fetal Ultrasound Grand Challenge: Semi-Supervised Cervical Segmentation (FUGC25). IEEE International Symposium on Biomedical Imaging (ISBI 2025). Zenodo. https://doi.org/10.5281/zenodo.14328192
  2. Bai, J., Lekadir, K., Ni, D., Slimani, S., Campello, V. M., Ohene-Botwe, B., Lu, Y., Chen, G., Hou, H., Qiu, D., & Zhou, Z. (2024). Intrapartum Ultrasound Grand Challenge 2024. 27th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2024). Zenodo. https://doi.org/10.5281/zenodo.10979813
  3. Bai, J., Ou, Z., Lu, Y., Ni, D., & Chen, G., (2023). Pubic Symphysis-Fetal Head Segmentation from Transperineal Ultrasound Images. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023 (MICCAI 2023). Zenodo. https://doi.org/10.5281/zenodo.7861699
  4. Chen S, Wang H, Long S, et al. Ultrasound Video Segmentation of Pubic Symphysis and Fetal Head for Angle of Progression Measurement[C]//Proceedings of the 6th ACM International Conference on Multimedia in Asia. 2024: 1-8.
  5. Bai J, Zhou Z, Ou Z, et al. PSFHS challenge report: Pubic symphysis and fetal head segmentation from intrapartum ultrasound images[J]. Medical Image Analysis, 2025, 99: 103353.
  6. Bai J, Kang X, Wang W, et al. A multimodal model in the prediction of the delivery mode using data from a digital twin-empowered labor monitoring system[J]. DIGITAL HEALTH, 2024, 10: 20552076241304934.
  7. Zhou Z, Lu Y, Bai J, et al. Segment anything model for fetal head-pubic symphysis segmentation in intrapartum ultrasound image analysis[J]. Expert Systems with Applications, 2024: 125699.
  8. Chen Z, Lu Y, Long S, et al. Dual-path multi-branch feature residual network for salient object detection[J]. Engineering Applications of Artificial Intelligence, 2024, 133: 108530.
  9. Ou Z, Bai J, Chen Z, et al (2024). RTSeg-Net: A Lightweight Network for Real-time Segmentation of Fetal Head and Pubic Symphysis from Intrapartum Ultrasound Images[J]. Computers in Biology and Medicine, 2024: 108501.
  10. Qiu, R., Zhou, M., Bai, J., Lu, Y. & Wang, H. (2024). HPSSP-Net: An Efficient Lightweight Network for Identifying Head-Pubic Symphysis Standard Plane from Intrapartum Ultrasound Images. Medical & Biological Engineering & Computing .
  11. Chen, Z., Ou, Z., Lu, Y., & Bai, J. (2024). Direction-guided and multi-scale feature screening for fetal head–pubic symphysis segmentation and angle of progression calculation. Expert Systems with Applications, 245, 123096.

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Biomedical Engineering and Bioengineering
Technology and Engineering > Biological and Physical Engineering > Biomedical Engineering and Bioengineering