Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning
Open accessAbstract
Placental volume measured with 3D ultrasound in the first trimester has been shown to be correlated to adverse pregnancy outcomes. This could potentially be used as a screening test to predict the “at risk” pregnancy. However, manual segmentation whilst previously shown to be accurate and repeatable is very time consuming and semi-automated methods still require operator input. To generate a screening tool, fully automated placental segmentation is required. In this work, a deep convolutional neural network (cNN), DeepMedic, was trained using the output of the semi-automated Random Walker method as ground truth. 300 3D ultrasound scans of first trimester placentas were used to train, validate and test the cNN. Compared against the semi-automated segmentation, resultant median (1stQuartile, 3rdQuartile) Dice Similarity Coefficient was 0.73 (0.66, 0.76). The median (1stQuartile, 3rdQuartile) Hausdorff distance was 27 mm (18 mm, 36 mm). We present the first attempt at using a deep cNN for segmentation of 3D ultrasound of the placenta. This work shows that feasible results compared to ground truth were obtained that could form the basis of a fully automatic segmentation method.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Efforts to segment the placenta using different fCNNs have been recently presented, but both used small data sets.
“Efforts to segment the placenta using different fCNNs have been recently presented, but both used small data sets (5, 6).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [5]
The median DSC of OxNNet was 0.84, a considerable improvement upon previously reported values of 0.64 and 0.73, demonstrating increased similarity between the PlVols estimated by OxNNet and those generated by the ground-truth RW algorithm.
“The median DSC of OxNNet was 0.84, a considerable improvement upon previously reported values of 0.64 (6) and 0.73 (5), demonstrating increased similarity between the PlVols estimated by OxNNet and those generated by the ground-truth RW algorithm.”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [5]
There have been semi-automated and fully automated methods proposed for placental segmentation from 3D ultrasound, with varying results.
“There have been some semi-automated and fully automated methods proposed for the segmentation task, with varying results [10,11,12,13,14,15,16,17].”
From Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants (2022) · cites this paper as [11]
The Dice results from the proposed segmentation method were higher than those of other fully automatic methods.
“Our Dice results from the segmentation task were higher than other alternatives for fully automatic methods [11,12,13,14,17].”
From Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants (2022) · cites this paper as [11]
The Hausdorff distance of the proposed method was better than that of [11].
“Our Hausdorff distance was better than [11] and comparable to [12], while our mean boundary distance was worse than [12].”
From Fully Automated Placental Volume Quantification From<scp>3D</scp>Ultrasound for Prediction of Small‐for‐Gestational‐Age Infants (2022) · cites this paper as [11]
Alternative methods based on deep learning have been proposed for placental segmentation.
“and a few alternative methods based on the recently popular deep learning methods have been proposed. 11 -14”
From Minimally interactive placenta segmentation from three-dimensional ultrasound images (2020) · cites this paper as [11]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation
Kamnitsas, K., Ledig, C., Newcombe, V. F. J., Simpson, J. P., Kane, A. D., Menon, D. K., Rueckert, D., & Glocker, B. (2017) · Medical Image Analysis · doi:10.1016/j.media.2016.10.004 · Cited by 3,498 · cited 11× · see citing works
“The results we report seem to be concordant with previously reported work [8, 9] which would indicate that this heterogeneous organ position seems to be a minor challenge that is overcome using this segmentation technique.”
BackgroundMethodResultInfluentialRandom Walks for Image Segmentation
Grady, L. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.233 · Cited by 2,624 · see citing works
Imaging of the Placenta: A Multimodality Pictorial Review
Elsayes, K. M., Trout, A. T., Friedkin, A. M., Liu, P. S., Bude, R. O., Platt, J. F., & Menias, C. O. (2009) · Radiographics · doi:10.1148/rg.295085242 · Cited by 221 · see citing works
Placental Volume at 11–13 Weeks’ Gestation in the Prediction of Birth Weight Percentile
Plasencia, W., Akolekar, R., Dagklis, T., Veduta, A., & Nicolaides, K. H. (2011) · Fetal Diagnosis and Therapy · doi:10.1159/000324318 · Cited by 86 · see citing works
Self-citationFast Fully Automatic Segmentation of the Human Placenta from Motion Corrupted MRI
Alansary, A., Kamnitsas, K., Davidson, A., Khlebnikov, R., Rajchl, M., Malamateniou, C., Rutherford, M., Hajnal, J. V., Glocker, B., Rueckert, D., et al. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_68 · Cited by 77 · see citing works
Odeh, M., Ophir, E., Maximovsky, O., Grinin, V., & Bornstein, J. (2011) · Prenatal Diagnosis · doi:10.1002/pd.2697 · Cited by 66 · see citing works
Yaqub, M. K., Javaid, M. K., Cooper, C. C., & Noble, J. A. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2284025 · Cited by 61 · 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
Self-citationAutomatic Segmentation of Antenatal 3-D Ultrasound Images
Anquez, J., Angelini, E. D., Grangé, G., & Isabelle Bloch, A. (2013) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2237400 · Cited by 42 · see citing works
Collins, S., Stevenson, G. N., Noble, J. A., & Impey, L. W. M. (2013) · Placenta · doi:10.1016/j.placenta.2012.11.033 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Deep Learning in Medical Ultrasound Analysis: A Review
Liu, S., Wang, Y., Yang, X., Lei, B., Liu, L., Li, S. X., Ni, D., & Wang, T. (2019) · Engineering · doi:10.1016/j.eng.2018.11.020 · Cited by 798 · see citing works
Deep Learning and Medical Diagnosis: A Review of Literature
Bakator, M., & Radosav, D. (2018) · Multimodal Technologies and Interaction · doi:10.3390/mti2030047 · Cited by 509 · see citing works
Machine learning for medical ultrasound: status, methods, and future opportunities
Brattain, L. J., Telfer, B. A., Dhyani, M., Grajo, J. R., & Samir, A. E. (2018) · Abdominal Radiology · doi:10.1007/s00261-018-1517-0 · Cited by 262 · see citing works
Deep Learning in Medical Ultrasound Image Analysis: A Review
Wang, Y., Ge, X., Ma, H., Qi, S., Zhang, G., & Yao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3071301 · Cited by 162 · 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 · see citing works
“73 (5), while another demonstrated a DSC of 0.”
BackgroundMethodSelf-citationNamburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.006 · Cited by 95 · see citing works
Segmentation 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
Kumar, V., Webb, J. M., Gregory, A., Meixner, D. D., Knudsen, J. M., Callstrom, M. R., Fatemi, M., & Alizad, A. (2020) · IEEE Access · doi:10.1109/access.2020.2982390 · Cited by 95 · see citing works
Zhang, Z., & Han, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2977962 · Cited by 83 · see citing works
Tun, W. M., Poologasundarampillai, G., Bischof, H., Nye, G., King, O. N. F., Basham, M., Tokudome, Y., Lewis, R. M., Johnstone, E., Brownbill, P., et al. (2021) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0140 · Cited by 43 · see citing works
Torrents-Barrena, J., Monill, N., Piella, G., Gratacós, E., Eixarch, E., Ceresa, M., & González Ballester, M. A. (2021) · Academic Radiology · doi:10.1016/j.acra.2019.11.006 · Cited by 42 · see citing works
Looney, P., Yin, Y., Collins, S. L., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Stevenson, G. N. (2021) · IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control · doi:10.1109/tuffc.2021.3052143 · Cited by 40 · see citing works
Self-citationAutomatic Segmentation of Human Placenta Images With U-Net
Han, M., Bao, Y., Sun, Z., Wen, S., Xia, L., Zhao, J., Du, J., & Yan, Z. (2019) · IEEE Access · doi:10.1109/access.2019.2958133 · Cited by 35 · 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
Self-citationAI-PLAX: AI-based placental assessment and examination using photos
Chen, Y., Zhang, Z., Wu, C., Davaasuren, D., Goldstein, J. A., Gernand, A. D., & Wang, J. Z. (2020) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101744 · Cited by 32 · see citing works
Awasthi, N., Vermeer, L., Fixsen, L. S., Lopata, R. G. P., & Pluim, J. P. W. (2022) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2022.3169684 · Cited by 31 · see citing works
Volume and vascularity: Using ultrasound to unlock the secrets of the first trimester placenta
Mathewlynn, S., & Collins, S. L. (2019) · Placenta · doi:10.1016/j.placenta.2019.06.379 · Cited by 30 · see citing works
Self-citationHu, R., Singla, R., Yan, R., Mayer, C., & Rohling, R. N. (2019) · 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857448 · Cited by 27 · see citing works
Stevenson, G. N., Noble, J. A., Welsh, A. W., Impey, L. W. M., & Collins, S. (2018) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2017.11.018 · Cited by 24 · see citing works
Self-citationAmniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities
Khan, I. U., Aslam, N., Anis, F. M., Mirza, S., AlOwayed, A., Aljuaid, R. M., & Bakr, R. M. (2022) · Sensors · doi:10.3390/s22124570 · Cited by 21 · see citing works
Intra-operative resection margin model of tongue carcinoma using 3D reconstructed ultrasound
Bekedam, N. M., Smit, J. N., de Koekkoek‐Doll, P. K., van Alphen, M. J. A., van Veen, R. L. P., Karssemakers, L. H. E., Karakullukçu, B., & Smeele, L. E. (2021) · Advances in Oral and Maxillofacial Surgery · doi:10.1016/j.adoms.2021.100154 · Cited by 14 · see citing works
Combining 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
Minimally interactive placenta segmentation from three-dimensional ultrasound images
Oguz, I., Yushkevich, N., Pouch, A. M., Oguz, B. U., Wang, J., Parameshwaran, S., Gee, J. C., Yushkevich, P. A., & Schwartz, N. (2020) · Journal of Medical Imaging · doi:10.1117/1.jmi.7.1.014004 · Cited by 13 · see citing works
Turkan, M., Dandıl, E., Urfalı, F. E., & Korkmaz, M. (2025) · IEEE Access · doi:10.1109/access.2025.3553548 · Cited by 14 · see citing works
Automatic ultrasound assessment of placenta previa during the third trimester for rural areas
Saavedra, A. C., Arroyo, J., Tamayo, L., Egoavil, M., Ramos, B., & Castaneda, B. (2020) · 2020 IEEE International Ultrasonics Symposium (IUS) · doi:10.1109/ius46767.2020.9251764 · Cited by 12 · see citing works
Schwartz, N., Oguz, I., Wang, J., Pouch, A., Yushkevich, N., Parameshwaran, S., Gee, J., Yushkevich, P., & Oguz, B. (2022) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15835 · Cited by 11 · see citing works
Snider, E. J., Torres, S. I. H., Avital, G., & Boice, E. N. (2022) · Journal of Imaging · doi:10.3390/jimaging8090252 · Cited by 9 · see citing works
Torrents-Barrena, J., Piella, G., Masoller, N., Gratacos, E., Eixarch, E., Ceresa, M., & Ballester, M. A. G. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) · doi:10.1109/isbi.2019.8759296 · Cited by 9 · see citing works
Torrents‐Barrena, J., Piella, G., Valenzuela‐Alcaráz, B., Gratacós, E., Eixarch, E., Ceresa, M., & Ballester, M. Á. G. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3001028 · Cited by 7 · see citing works
Fetal Movement Detection and Anatomical Plane Recognition using YOLOv5 Network in Ultrasound Scans
Dandıl, E., Turkan, M., Urfalı, F. E., Bıyık, İ., & Korkmaz, M. (2021) · European Journal of Science and Technology · doi:10.31590/ejosat.951786 · Cited by 7 · see citing works
Multiple Instance Segmentation in Brachial Plexus Ultrasound Image Using BPMSegNetPreprint
Ding, Y., Yang, Q., Wu, G., Zhang, J., & Qin, Z. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12012 · Cited by 7 · see citing works
The Classification and Segmentation of Fetal Anatomies Ultrasound Image: A Survey
Song, C., Gao, T., Wang, H., Sudirman, S., Zhang, W., & Zhu, H. (2020) · Journal of Medical Imaging and Health Informatics · doi:10.1166/jmihi.2021.3616 · Cited by 6 · see citing works
Fully Automatic Segmentation of 3D Brain Ultrasound: Learning from Coarse AnnotationsPreprint
Rackerseder, J., Göbl, R., Navab, N., & Hennersperger, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.08655 · Cited by 6 · see citing works
Marsousi, M., Plataniotis, K. N., & Stergiopoulos, S. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2805777 · Cited by 5 · see citing works
Importance of Deep Learning Models to Perform Segmentation on Medical Imaging Modalities
Sharma, P., & Bhatt, D. P. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-16-2641-8_56 · Cited by 4 · see citing works
Prognosis of Anomaly Detection in Tiny Life using AI based Fetal Segmentation Approach
Bhargavi, D., Manikanta, K., Garg, K., & Arora, M. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.04.461 · Cited by 3 · see citing works
Neural Network Techniques in Medical Image Processing
Nagar, S., Jain, M. K., & Nirvikar (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1275-9_38 · Cited by 3 · see citing works
Deep Learning Approaches and Biomarkers in Medical Diagnosis
Malik, P., & Singh, S. (2023) · Recent Patents on Engineering · doi:10.2174/1872212117666230130100048 · Cited by 2 · see citing works
Segmentation of the placenta and its vascular tree in Doppler ultrasound for fetal surgery planning
Perera-Bel, E., Ceresa, M., Torrents‐Barrena, J., Masoller, N., Valenzuela‐Alcaráz, B., Gratacós, E., Eixarch, E., & Ballester, M. Á. G. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02256-2 · Cited by 2 · see citing works
An Introduction to Deep Learning Techniques in Ultrasound Image Modality
Rai, M., Datta, P., & Ansari, R. (2019) · International Conference on Power Energy, Environment and Intelligent Control (PEEIC) · doi:10.1109/peeic47157.2019.8976806 · Cited by 1 · see citing works
Rectal Carcinoma Segmentation Based On Image Pre-processing and Improved U-Net with Residual Block
Runchen, L., Hao, W., He, G., Ahmad, K. M., & Yu, W. (2021) · International Conference on Machine Learning and Intelligent Systems Engineering (MLISE) · doi:10.1109/mlise54096.2021.00034 · Cited by 1 · see citing works
Review on Ultrasound-Image Analysis of Fetus Using Deep Learning
Gautam, L., & Kaur, G. (2023) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct57861.2023.10126500 · Cited by 1 · see citing works
Improvised convolutional auto encoder for thyroid nodule image enhancement and segmentation
Gunjali, D., & Hamsavath, P. N. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i1.pp342-351
Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images
Li, H., Oguz, B. U., Arenas, G. A., Yao, X., Wang, J., Pouch, A. M., Byram, B. C., Schwartz, N., & Oğuz, İ. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73647-6_13
Automatic Segmentation of Human Placenta from 3D Multimodal Ultrasound Data
Singh, S., Stevenson, G. N., Mian, B., Welsh, A. W., & Sowmya, A. (2024) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta63115.2024.00045
Self-citationBastiaansen, W. A. P., de Vos, E. S., Koning, A. H. J., Berns, L., Klein, S., Mulders, A. G. M. G. J., Steegers-Theunissen, R. P. M., & Rousian, M. (2026) · Placenta · doi:10.1016/j.placenta.2026.05.022
Prediction of Placenta Maturity Grade Using Multimodal Data: USG Image and Clinical Report
Kadampur, S. M., Raikar, M. M., & Baligar, V. P. (2025) · IEEE Conference on Engineering Informatics (ICEI) · doi:10.1109/icei68356.2025.11603686
- Authors
- Looney, P., Stevenson, G. N., Nicolaides, K. H., Plasencia, W., Molloholli, M., Natsis, S., & Collins, S. L.
- Affiliations
- UNSW Sydney (AU)
- Harris Birthright Research Centre for Fetal Medicine (GB)
- Hospiten Group (ES)
- Wexham Park Hospital (GB)
- John Radcliffe Hospital (GB)
- Venue
- 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)
- Publisher
- IEEE
- Published
- 2017-04
- Pages
- 279-282
- OpenAlex
- W2708499541
- Cited by
- 47
- References
- 10
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 2 · 2022: 4 · 2021: 8 · 2020: 11 · 2019: 8 · 2018: 8
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-nd
- Locations
- IEEE International Symposium on Biomedical Imaging (ISBI) (publishedVersion)
- UNSWorks (UNSW Sydney) (submittedVersion, cc-by-nc-nd)
- Funders
- National Institutes of Health
- National Institute of Child Health and Human Development (UO1-HD087209)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (UO1 HD 087209, U01HD087209)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- placenta segmentation, 3D ultrasound, first trimester, placental volume, DeepMedic, convolutional neural networks, random walker segmentation, automated medical image segmentation, pregnancy screening, adverse pregnancy outcomes
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
