{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T07:40:20Z","timestamp":1793086820124,"version":"4.5.0"},"reference-count":34,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,9,23]],"date-time":"2023-09-23T00:00:00Z","timestamp":1695427200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007762","name":"Instituto Nacional de Ci\u00eancia e Tecnologia Translacional em Medicina","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007762","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008473","name":"Gebauer Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008473","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003006","name":"Eidgen\u00f6ssische Technische Hochschule Z\u00fcrich","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003006","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","academicradiology.org","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1016\/j.acra.2023.09.035","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T09:16:17Z","timestamp":1699002977000},"page":"1400-1409","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"title":["AI-Supported Autonomous Uterus Reconstructions: First Application in MRI Using 3D SPACE with Iterative Denoising"],"prefix":"10.1016","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3816-3302","authenticated-orcid":false,"given":"Daniel","family":"Hausmann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aline","family":"Lerch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sebastian","family":"Hitziger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monika","family":"Farkas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elisabeth","family":"Weiland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Lemke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Grimm","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahel A.","family":"Kubik-Huch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.acra.2023.09.035_bib1","doi-asserted-by":"crossref","first-page":"792","DOI":"10.1007\/s00330-018-5515-y","article-title":"Endometrial cancer MRI staging: updated guidelines of the european society of urogenital radiology","volume":"29","author":"Nougaret","year":"2019","journal-title":"Eur Radiol"},{"issue":"5","key":"10.1016\/j.acra.2023.09.035_bib2","doi-asserted-by":"crossref","first-page":"1102","DOI":"10.1007\/s00330-010-1998-x","article-title":"Staging of uterine cervical cancer with MRI: guidelines of the european society of urogenital radiology","volume":"21","author":"Balleyguier","year":"2011","journal-title":"Eur Radiol"},{"issue":"8","key":"10.1016\/j.acra.2023.09.035_bib3","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1007\/s00330-017-5157-5","article-title":"European Society of Urogenital Radiology (ESUR) Guidelines: MR imaging of leiomyomas","volume":"28","author":"Kubik-Huch","year":"2018","journal-title":"Eur Radiol"},{"issue":"7","key":"10.1016\/j.acra.2023.09.035_bib4","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1007\/s00330-009-1309-6","article-title":"Staging of endometrial cancer with MRI: guidelines of the European Society Of Urogenital Imaging","volume":"19","author":"Kinkel","year":"2009","journal-title":"Eur Radiol"},{"issue":"10","key":"10.1016\/j.acra.2023.09.035_bib5","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1097\/RLI.0000000000000786","article-title":"Evaluation of iterative denoising 3-dimensional T2-weighted turbo spin echo for the diagnosis of deep infiltrating endometriosis","volume":"56","author":"Florin","year":"2021","journal-title":"Invest Radiol"},{"issue":"6","key":"10.1016\/j.acra.2023.09.035_bib6","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.acra.2022.06.015","article-title":"Quantitative assessment of iteratively denoised 3D SPACE with inner-volume excitation and simultaneous multi-slice BLADE for optimizing female pelvis magnetic resonance imaging at 1.5 T","volume":"30","author":"Hausmann","year":"2023","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2023.09.035_bib7","doi-asserted-by":"crossref","unstructured":"He K., Zhang X., Ren S., Sun J. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition2016; 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.1016\/j.acra.2023.09.035_bib8","doi-asserted-by":"crossref","unstructured":"Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention: Springer; 2015; 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"7","key":"10.1016\/j.acra.2023.09.035_bib9","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1109\/TBME.2015.2496253","article-title":"Scalable high-performance image registration framework by unsupervised deep feature representations learning","volume":"63","author":"Wu","year":"2016","journal-title":"IEEE Trans Biomed Eng"},{"issue":"8","key":"10.1016\/j.acra.2023.09.035_bib10","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.3390\/diagnostics11081484","article-title":"Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging","volume":"11","author":"Herrmann","year":"2021","journal-title":"Diagnostics"},{"key":"10.1016\/j.acra.2023.09.035_bib11","article-title":"Learning implicit brain MRI manifolds with deep learning","volume":"10574","author":"Bermudez","year":"2018","journal-title":"Proc SPIE Int Soc Opt Eng"},{"key":"10.1016\/j.acra.2023.09.035_bib12","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.964250","article-title":"Triplanar U-Net with lesion-wise voting for the segmentation of new lesions on longitudinal MRI studies","volume":"16","author":"Hitziger","year":"2022","journal-title":"Front Neurosci"},{"issue":"6","key":"10.1016\/j.acra.2023.09.035_bib13","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.jvir.2019.11.032","article-title":"Deep learning based on MR imaging for predicting outcome of uterine fibroid embolization","volume":"31","author":"Luo","year":"2020","journal-title":"J Vasc Interv Radiol"},{"issue":"1","key":"10.1016\/j.acra.2023.09.035_bib14","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1186\/s12880-022-00808-3","article-title":"The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists","volume":"22","author":"Urushibara","year":"2022","journal-title":"BMC Med Imaging"},{"key":"10.1016\/j.acra.2023.09.035_bib15","doi-asserted-by":"crossref","unstructured":"Chollet F. Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition2017; 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"issue":"11","key":"10.1016\/j.acra.2023.09.035_bib16","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1016\/j.acra.2019.01.012","article-title":"Automatic breast and fibroglandular tissue segmentation in breast mri using deep learning by a fully-convolutional residual neural network U-Net","volume":"26","author":"Zhang","year":"2019","journal-title":"Acad Radiol"},{"issue":"4","key":"10.1016\/j.acra.2023.09.035_bib17","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1002\/jmri.26337","article-title":"Fully automatic segmentation on prostate MR images based on cascaded fully convolution network","volume":"49","author":"Zhu","year":"2019","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2023.09.035_bib18","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.mri.2019.04.009","article-title":"Open-source pipeline for multi-class segmentation of the spinal cord with deep learning","volume":"64","author":"Paugam","year":"2019","journal-title":"Magn Reson Imaging"},{"issue":"1","key":"10.1016\/j.acra.2023.09.035_bib19","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1148\/radiol.2018172322","article-title":"Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry","volume":"288","author":"Norman","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2023.09.035_bib20","series-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2016","first-page":"424","article-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation. International conference on medical image computing and computer-assisted intervention","volume":"9901","author":"\u00c7i\u00e7ek","year":"2016"},{"issue":"5","key":"10.1016\/j.acra.2023.09.035_bib21","article-title":"Deep learning-based segmentation of the placenta and uterus on MR images","volume":"8","author":"Shahedi","year":"2021","journal-title":"J Med Imaging (Bellingham)"},{"key":"10.1016\/j.acra.2023.09.035_bib22","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2019.103438","article-title":"Automatic segmentation of the uterus on MRI using a convolutional neural network","volume":"114","author":"Kurata","year":"2019","journal-title":"Comput Biol Med"},{"issue":"1","key":"10.1016\/j.acra.2023.09.035_bib23","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1038\/s41598-020-80068-9","article-title":"Automated segmentation of endometrial cancer on MR images using deep learning","volume":"11","author":"Hodneland","year":"2021","journal-title":"Sci Rep"},{"issue":"10","key":"10.1016\/j.acra.2023.09.035_bib24","doi-asserted-by":"crossref","first-page":"5374","DOI":"10.1007\/s00330-020-06869-8","article-title":"Patient preparation and image quality in female pelvic MRI: recommendations revisited","volume":"30","author":"Sheikh-Sarraf","year":"2020","journal-title":"Eur Radiol"},{"issue":"2","key":"10.1016\/j.acra.2023.09.035_bib25","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.acra.2023.09.035_bib26","doi-asserted-by":"crossref","unstructured":"Ronneberger O., Fischer P., Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18: Springer; 2015; 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"10.1016\/j.acra.2023.09.035_bib27","unstructured":"Oktay O., Schlemper J., Folgoc L.L., et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999. 2018."},{"key":"10.1016\/j.acra.2023.09.035_bib28","doi-asserted-by":"crossref","unstructured":"Barz B., Denzler J. Deep learning on small datasets without pre-training using cosine loss. Proceedings of the IEEE\/CVF winter conference on applications of computer vision2020; 1371\u20131380.","DOI":"10.1109\/WACV45572.2020.9093286"},{"issue":"4","key":"10.1016\/j.acra.2023.09.035_bib29","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1002\/jmri.24542","article-title":"Optimized three-dimensional fast-spin-echo MRI","volume":"39","author":"Mugler","year":"2014","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"10.1016\/j.acra.2023.09.035_bib30","doi-asserted-by":"crossref","first-page":"254","DOI":"10.2214\/AJR.09.3226","article-title":"MRI of the pelvis in women: 3D versus 2D T2-weighted technique","volume":"195","author":"Proscia","year":"2010","journal-title":"Am J Roentgenol"},{"issue":"2","key":"10.1016\/j.acra.2023.09.035_bib31","first-page":"969","article-title":"Image quality assessment of 2D versus 3D T2WI and evaluation of ultra-high b-value (b=2,000 mm\/s(2)) DWI for response assessment in rectal cancer","volume":"38","author":"Hausmann","year":"2018","journal-title":"Anticancer Res"},{"key":"10.1016\/j.acra.2023.09.035_bib32","unstructured":"Vaussy A., Florin M., Macron L., et al. Accelerated 3D T2 SPACE CAIPIRINHA with Iterative Denoising for the Assessment of Deep Infiltrating Endometriosis."},{"issue":"5","key":"10.1016\/j.acra.2023.09.035_bib33","first-page":"12","article-title":"Deep learning-based total kidney volume segmentation in autosomal dominant polycystic kidney disease using attention, cosine loss, and sharpness aware minimization","author":"Raj","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.acra.2023.09.035_bib34","unstructured":"Foret P, Kleiner A, Mobahi H, Neyshabur B. Sharpness-aware minimization for efficiently improving generalization. arXiv preprint arXiv:201001412. 2020."}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633223005147?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633223005147?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,4,10]],"date-time":"2024-04-10T23:06:14Z","timestamp":1712790374000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633223005147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":34,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["S1076633223005147"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2023.09.035","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AI-Supported Autonomous Uterus Reconstructions: First Application in MRI Using 3D SPACE with Iterative Denoising","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2023.09.035","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Association of University Radiologists. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}