{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:52:22Z","timestamp":1774158742387,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T00:00:00Z","timestamp":1651276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020194","name":"Wellcome\/EPSRC Centre for Interventional and Surgical Sciences","doi-asserted-by":"crossref","award":["203145\/Z\/16\/Z"],"award-info":[{"award-number":["203145\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100020194","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P027938\/1, EP\/R004080\/1, EP\/P012841\/1"],"award-info":[{"award-number":["EP\/P027938\/1, EP\/R004080\/1, EP\/P012841\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2022,5]]},"abstract":"Abstract<\/jats:title>\n Purpose<\/jats:title>\n In obstetric ultrasound (US) scanning, the learner\u2019s ability to mentally build a three-dimensional (3D) map of the fetus from a two-dimensional (2D) US image represents a major challenge in skill acquisition. We aim to build a US plane localisation system for 3D visualisation, training, and guidance without integrating additional sensors.<\/jats:p>\n <\/jats:sec>\n Methods<\/jats:title>\n We propose a regression convolutional neural network (CNN) using image features to estimate the six-dimensional pose of arbitrarily oriented US planes relative to the fetal brain centre. The network was trained on synthetic images acquired from phantom 3D US volumes and fine-tuned on real scans. Training data was generated by slicing US volumes into imaging planes in Unity at random coordinates and more densely around the standard transventricular (TV) plane.<\/jats:p>\n <\/jats:sec>\n Results<\/jats:title>\n With phantom data, the median errors are 0.90 mm\/1.17$$^\\circ $$<\/jats:tex-math>\n \n \n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a0and 0.44 mm\/1.21$$^\\circ $$<\/jats:tex-math>\n \n \n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a0for random planes and planes close to the TV one, respectively. With real data, using a different fetus with the same gestational age (GA), these errors are 11.84 mm\/25.17$$^\\circ $$<\/jats:tex-math>\n \n \n \u2218<\/mml:mo>\n <\/mml:msup>\n <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. The average inference time is 2.97 ms per plane.<\/jats:p>\n <\/jats:sec>\n Conclusion<\/jats:title>\n The proposed network reliably localises US planes within the fetal brain in phantom data and successfully generalises pose regression for an unseen fetal brain from a similar GA as in training. Future development will expand the prediction to volumes of the whole fetus and assess its potential for vision-based, freehand US-assisted navigation when acquiring standard fetal planes.<\/jats:p>\n <\/jats:sec>","DOI":"10.1007\/s11548-022-02609-z","type":"journal-article","created":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T05:22:23Z","timestamp":1651296143000},"page":"833-839","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Deep learning-based plane pose regression in obstetric ultrasound"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2853-9791","authenticated-orcid":false,"given":"Chiara","family":"Di Vece","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9494-9724","authenticated-orcid":false,"given":"Brian","family":"Dromey","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4609-1177","authenticated-orcid":false,"given":"Francisco","family":"Vasconcelos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0199-6140","authenticated-orcid":false,"given":"Anna L.","family":"David","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8562-1970","authenticated-orcid":false,"given":"Donald","family":"Peebles","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0980-3227","authenticated-orcid":false,"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,30]]},"reference":[{"key":"2609_CR1","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/0002-9378(85)90298-4","volume":"151","author":"FP Hadlock","year":"1985","unstructured":"Hadlock FP, Harrist RB, Sharman RS, Deter RL, Park SK (1985) Estimation of fetal weight with the use of head body and femur measurements: a prospective study. 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