Automatic Segmentation Of the Placenta and its Peripheral Vasculature in Volumetric Ultrasound for TTTS Fetal Surgery
Abstract
Twin-to-twin transfusion syndrome is a serious condition that can affect pregnancies when identical twins share the placenta. In these cases, abnormal placental vessel connections (anastomoses) cause an uneven blood distribution between the babies. Ultrasound (US) enormously facilitates the assessment of these cases, but placenta segmentation is still a challenging task due to artifacts and high variability in its position, orientation, shape and appearance. We propose for the first time a fully-automated framework to achieve an accurate segmentation of the placenta and its peripheral vasculature in 3D US. A conditional Generative Adversarial Network is used to automatically identify the placenta. Afterwards, the entire vasculature is extracted using Modified Spatial Kernelized Fuzzy C-Means and Markov Random Fields. The method is tested on singleton and twin pregnancies from 15 to 38 gestational weeks, achieving a mean Dice coefficient of 0.75 ± 0.12 and 0.70 ± 0.14 for the placenta and its vessels.
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Image-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · see citing works
Gibbs and Markov random systems with constraints
Moussouris, J. (1974) · Journal of Statistical Physics · doi:10.1007/bf01011714 · Cited by 190 · see citing works
Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L. (2018) · JCI Insight · doi:10.1172/jci.insight.120178 · Cited by 111 · cited 2× · see citing works
“Similar Dice coefficients compared to [4] and [5] are obtained by using less training data.”
BackgroundResultEarly prediction of twin‐to‐twin transfusion syndrome: systematic review and meta‐analysis
Stagnati, V., Zanardini, C., Fichera, A., et al. (2016) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.15989 · Cited by 63 · see citing works
Towards Automatic Semantic Segmentation in Volumetric Ultrasound
Yang, X., Yu, L., Li, S., Wang, X., Wang, N., Qin, J., Ni, D., & Heng, P.-A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-66182-7_81 · Cited by 61 · see citing works
Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning
Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L. (2017) · 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) · doi:10.1109/isbi.2017.7950519 · Cited by 47 · see citing works
Stevenson, G. N., Collins, S. L., Ding, J., Impey, L., & Noble, J. A. (2015) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2015.07.021 · Cited by 46 · see citing works
Castro, A., Bóveda, C., & Arcay, B. (2010) · IEEE International Conference on Information Technology and Applications in Biomedicine · doi:10.1109/itab.2010.5687726 · Cited by 7 · see citing works
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Deep Learning Strategies for Ultrasound in Pregnancy
Diniz, P. H. B., Yin, Y., & Collins, S. (2020) · EMJ Reproductive Health · doi:10.33590/emjreprohealth/20-00100 · Cited by 32 · see citing works
- Authors
- Torrents-Barrena, J., Piella, G., Masoller, N., Gratacos, E., Eixarch, E., Ceresa, M., & Ballester, M. A. G.
- Affiliations
- Universitat Pompeu Fabra (ES)
- Hospital Sant Joan de Déu Barcelona (ES)
- Venue
- 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
- Publisher
- IEEE
- Published
- 2019-04
- Pages
- 772-775
- OpenAlex
- W2957083320
- Cited by
- 9
- References
- 8
- Citations by year
- 2025: 2 · 2024: 2 · 2022: 2 · 2020: 3
- FWCI
- 0.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Keywords
- twin-twin transfusion syndrome, placenta segmentation, placental vasculature, volumetric ultrasound imaging, fetal surgery, conditional generative adversarial networks, fuzzy c-means clustering, Markov random fields, 3D ultrasound
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