{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:12:29Z","timestamp":1790694749676,"version":"4.1.0"},"reference-count":28,"publisher":"American Society for Clinical Investigation","issue":"11","funder":[{"name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Human Placenta Project of the National Institutes of Health","award":["UO1-HD087209"],"award-info":[{"award-number":["UO1-HD087209"]}]},{"name":"Leslie Stevens\u2019 Fund, Sydney","award":["philanthropic grant"],"award-info":[{"award-number":["philanthropic grant"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,6,7]]},"abstract":"<jats:p>We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the \u201cground-truth\u201d data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.<\/jats:p>","DOI":"10.1172\/jci.insight.120178","type":"journal-article","created":{"date-parts":[[2018,6,6]],"date-time":"2018-06-06T11:01:10Z","timestamp":1528282870000},"source":"Crossref","is-referenced-by-count":93,"title":["Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning"],"prefix":"10.1172","volume":"3","author":[{"given":"P\u00e1draig","family":"Looney","sequence":"first","affiliation":[{"name":"Nuffield Department of Women\u2019s and Reproductive Health, University of Oxford, Women\u2019s Centre, John Radcliffe Hospital, Oxford, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gordon N.","family":"Stevenson","sequence":"additional","affiliation":[{"name":"School of Women\u2019s and Children\u2019s Health, University of New South Wales, Randwick, New South Wales, Australia."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kypros H.","family":"Nicolaides","sequence":"additional","affiliation":[{"name":"Harris Birthright Research Centre of Fetal Medicine, Kings College Hospital, London, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walter","family":"Plasencia","sequence":"additional","affiliation":[{"name":"Fetal Medicine Unit, Hospiten Group, Tenerife, Canary Islands, Spain."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malid","family":"Molloholli","sequence":"additional","affiliation":[{"name":"Fetal Medicine Unit, Women\u2019s Centre, John Radcliffe Hospital, Oxford, United Kingdom."},{"name":"Department of Obstetrics and Gynaecology, Wexham Park Hospital, Slough, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stavros","family":"Natsis","sequence":"additional","affiliation":[{"name":"Fetal Medicine Unit, Women\u2019s Centre, John Radcliffe Hospital, Oxford, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally L.","family":"Collins","sequence":"additional","affiliation":[{"name":"Nuffield Department of Women\u2019s and Reproductive Health, University of Oxford, Women\u2019s Centre, John Radcliffe Hospital, Oxford, United Kingdom."},{"name":"Fetal Medicine Unit, Women\u2019s Centre, John Radcliffe Hospital, Oxford, United Kingdom."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"232","published-online":{"date-parts":[[2018,6,7]]},"reference":[{"key":"B1","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Proceedings. https:\/\/papers.nips.cc\/paper\/4824-imagenet-classification-with-deep-convolutional-neural-networks Accessed April 25, 2018"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"B3"},{"key":"B4"},{"key":"B5","unstructured":"Looney P, et al. Automatic 3D ultrasound segmentation of the first trimester placenta using deep learning. IEEE Xplore. https:\/\/ieeexplore.ieee.org\/abstract\/document\/7950519\/ Accessed May 16, 2018"},{"key":"B6"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1002\/uog.2641"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/uog.6455"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1053\/plac.2001.0684"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1097\/00004424-198103000-00005"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2012.09.003"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.1002\/pd.4754"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultrasmedbio.2015.07.021"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1159\/000324318"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1038\/nature21056"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"B18","unstructured":"Looney P. OxNNet. Github. https:\/\/github.com\/plooney\/oxnnet\/commit\/fbb84cd4d100447a997455df87478aa7cc3b2498 Accessed May 16, 2018"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1002\/uog.5331"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(97)11280-6"},{"key":"B21","doi-asserted-by":"publisher","DOI":"10.1002\/uog.1923"},{"key":"B22"},{"key":"B23"},{"key":"B24","unstructured":"Abadi M, et al. Tensorflow: Large-scale machine learning on heterogeneous distributed systems. Cornell University Library. https:\/\/arxiv.org\/abs\/1603.04467 Published March 14, 2016. Updated March 16, 2016. Accessed April 24, 2018"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2012.05.001"},{"key":"B26","unstructured":"R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http:\/\/www.R-project.org\/ Accessed April 25, 2018"},{"key":"B27","doi-asserted-by":"crossref","DOI":"10.1186\/1471-2105-12-77","article-title":"pROC: an open-source package for R and S+ to analyze and compare ROC curves","volume":"12","author":"Robin","year":"2011","journal-title":"BMC Bioinformatics"},{"key":"B28"}],"container-title":["JCI Insight"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/insight.jci.org\/articles\/view\/120178\/files\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T12:59:47Z","timestamp":1779195587000},"score":1,"resource":{"primary":{"URL":"https:\/\/insight.jci.org\/articles\/view\/120178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,7]]},"references-count":28,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,6,7]]},"published-print":{"date-parts":[[2018,6,7]]}},"alternative-id":["10.1172\/jci.insight.120178"],"URL":"https:\/\/doi.org\/10.1172\/jci.insight.120178","relation":{},"ISSN":["2379-3708"],"issn-type":[{"value":"2379-3708","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,7]]},"article-number":"e120178"}}