Towards Whole Placenta Segmentation at Late Gestation Using Multi-view Ultrasound Images
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Classification accuracy improves when the CNN is trained on either anterior or posterior placentas.
“Anterior placentas are generally more easily segmented, and classification accuracy improves when the CNN is trained on either anterior or posterior placentas11.”
From Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization (2023) · cites this paper as [11]
Anterior placentas are generally more easily segmented.
“Anterior placentas are generally more easily segmented, and classification accuracy improves when the CNN is trained on either anterior or posterior placentas11.”
From Multi-centre deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization (2023) · cites this paper as [11]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Towards automated semantic segmentation in prenatal volumetric ultrasound
Yang, X., Yu, L., Li, S., Wen, H., Luo, D., Bian, C., Qin, J., Ni, D., & Heng, P.-A. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2858779 · Cited by 115 · 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 1× · see citing works
“State-of-the-art segmentation methods using convolutional neural networks (CNNs) have been used in [7, 4] and in [13] additionally for the fetus and the gestational sac.”
BackgroundSegmentation and classification in MRI and US fetal imaging: Recent trends and future prospects
Torrents-Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2018.10.003 · Cited by 97 · see citing works
Volumetric Ultrasound Panorama Based on 3D SIFT
Ni, D., Qu, Y., Yang, X., Chui, Y.-P., Wong, T., Ho, S. S. M., & Heng, P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-85990-1_7 · Cited by 66 · see citing works
Three-Dimensional Ultrasound Mosaicing
Wachinger, C., Wein, W., & Navab, N. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75759-7_40 · Cited by 58 · 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
Torrents‐Barrena, J., Piella, G., Masoller, N., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.03.008 · Cited by 46 · see citing works
Placenta Maps: In Utero Placental Health Assessment of the Human Fetus
Miao, H., Mistelbauer, G., Karimov, A. K., et al. (2017) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2017.2674938 · Cited by 32 · see citing works
Multi-view Image Reconstruction: Application to Fetal Ultrasound Compounding
Zimmer, V. A., Gómez, A., Noh, Y., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00807-9_11 · Cited by 25 · see citing works
Self-citationCombining Deep Learning and Multi-atlas Label Fusion for Automated Placenta Segmentation from 3DUS
Oguz, B. U., Wang, J., Yushkevich, N., Pouch, A., Gee, J., Yushkevich, P. A., Schwartz, N., & Oguz, I. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-00807-9_14 · Cited by 13 · see citing works
Fast Registration of 3D Fetal Ultrasound Images Using Learned Corresponding Salient Points
Gómez, A., Bhatia, K. K., Tharin, S., et al. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67561-9_4 · Cited by 11 · see citing works
Regional Differences in End-Diastolic Volumes between 3D Echo and CMR in HLHS Patients
Gómez, A., Oktay, O., Rueckert, D., et al. (2016) · Frontiers in Pediatrics · doi:10.3389/fped.2016.00133 · Cited by 7 · see citing works
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Use of artificial intelligence and deep learning in fetal ultrasound imaging
Ramirez Zegarra, R., & Ghi, T. (2023) · Ultrasound in Obstetrics & Gynecology · doi:10.1002/uog.26130 · Cited by 102 · see citing works
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
Andreasen, L. A., Feragen, A., Christensen, A. N., Thybo, J. K., Svendsen, M. B. S., Zepf, K., Lekadir, K., & Tolsgaard, M. G. (2023) · Scientific Reports · doi:10.1038/s41598-023-29105-x · Cited by 28 · see citing works
- Authors
- Zimmer, V. A., Gomez, A., Skelton, E., Toussaint, N., Zhang, T., Khanal, B., Wright, R., Noh, Y., Ho, A., Matthew, J., Hajnal, J. V., & Schnabel, J. A.
- Affiliations
- King's College London (GB)
- Corresponding author
- Zimmer, Veronika A.
- ORCID
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2019
- Pages
- 628-636
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2980198536
- Cited by
- 20
- References
- 14
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 3 · 2022: 9 · 2021: 2 · 2020: 3
- FWCI
- 1.14
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Research Portal (King's College London) (submittedVersion)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion)
- Funders
- Engineering and Physical Sciences Research Council (EP/N026993/1, EP/M000133/1)
- 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
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Keywords
- placenta segmentation, late pregnancy, 3D convolutional neural networks, placental morphology, third trimester, prenatal ultrasound
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