{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:13:32Z","timestamp":1790273612519,"version":"4.1.0"},"reference-count":0,"publisher":"SPIE-Intl Soc Optical Eng","issue":"05","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Med. Imag."],"published-print":{"date-parts":[[2020,9,16]]},"abstract":"<jats:p>Purpose: We present an original method for simulating realistic fetal neurosonography images specifically generating third-trimester pregnancy ultrasound images from second-trimester images. Our method was developed using unpaired data, as pairwise data were not available. We also report original insights on the general appearance differences between second- and third-trimester fetal head transventricular (TV) plane images.\nApproach: We design a cycle-consistent adversarial network (Cycle-GAN) to simulate visually realistic third-trimester images from unpaired second- and third-trimester ultrasound images. Simulation realism is evaluated qualitatively by experienced sonographers who blindly graded real and simulated images. A quantitative evaluation is also performed whereby a validated deep-learning-based image recognition algorithm (ScanNav\u00ae) acts as the expert reference to allow hundreds of real and simulated images to be automatically analyzed and compared efficiently.\nResults: Qualitative evaluation shows that the human expert cannot tell the difference between real and simulated third-trimester scan images. 84.2% of the simulated third-trimester images could not be distinguished from the real third-trimester images. As a quantitative baseline, on 3000 images, the visibility drop of the choroid, CSP, and mid-line falx between real second- and real third-trimester scans was computed by ScanNav\u00ae and found to be 72.5%, 61.5%, and 67%, respectively. The visibility drop of the same structures between real second-trimester and simulated third-trimester was found to be 77.5%, 57.7%, and 56.2%, respectively. Therefore, the real and simulated third-trimester images were consider to be visually similar to each other. Our evaluation also shows that the third-trimester simulation of a conventional GAN is much easier to distinguish, and the visibility drop of the structures is smaller than our proposed method.\nConclusions: The results confirm that it is possible to simulate realistic third-trimester images from second-trimester images using a modified Cycle-GAN, which may be useful for deep learning researchers with a restricted availability of third-trimester scans but with access to ample second trimester images. We also show convincing simulation improvements, both qualitatively and quantitatively, using the Cycle-GAN method compared with a conventional GAN. Finally, the use of a machine learning-based reference (in the case ScanNav\u00ae) for large-scale quantitative image analysis evaluation is also a first to our knowledge.<\/jats:p>","DOI":"10.1117\/1.jmi.7.5.057001","type":"journal-article","created":{"date-parts":[[2020,9,16]],"date-time":"2020-09-16T04:46:26Z","timestamp":1600231586000},"page":"1-15","source":"Crossref","is-referenced-by-count":5,"title":["Simulating realistic fetal neurosonography images with appearance and growth change using cycle-consistent adversarial networks and an evaluation"],"prefix":"10.1117","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0495-3581","authenticated-orcid":false,"given":"Yangdi","family":"Xu","sequence":"first","affiliation":[{"name":"Univ. of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lok Hin","family":"Lee","sequence":"additional","affiliation":[{"name":"Univ. of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5588-1410","authenticated-orcid":false,"given":"Lior","family":"Drukker","sequence":"additional","affiliation":[{"name":"Univ. of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6896-1105","authenticated-orcid":false,"given":"Mohammad","family":"Yaqub","sequence":"additional","affiliation":[{"name":"Intelligent Ultrasound Ltd."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aris T.","family":"Papageorghiou","sequence":"additional","affiliation":[{"name":"Univ. of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3060-3772","authenticated-orcid":false,"given":"Alison J.","family":"Noble","sequence":"additional","affiliation":[{"name":"Univ. of Oxford"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"189","container-title":["Journal of Medical Imaging"],"original-title":[],"link":[{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-7\/issue-05\/057001\/Simulating-realistic-fetal-neurosonography-images-with-appearance-and-growth-change\/10.1117\/1.JMI.7.5.057001.full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:57:48Z","timestamp":1790272668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/journals\/journal-of-medical-imaging\/volume-7\/issue-05\/057001\/Simulating-realistic-fetal-neurosonography-images-with-appearance-and-growth-change\/10.1117\/1.JMI.7.5.057001.full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,16]]},"references-count":0,"journal-issue":{"issue":"05"},"URL":"https:\/\/doi.org\/10.1117\/1.jmi.7.5.057001","relation":{},"ISSN":["2329-4302","2329-4310"],"issn-type":[{"value":"2329-4302","type":"print"},{"value":"2329-4310","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,16]]}}}