{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T12:55:17Z","timestamp":1789390517353,"version":"build-2803163510"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"DOI":"10.1007\/s00330-022-08976-0","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T19:02:58Z","timestamp":1657652578000},"page":"54-63","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Improved differentiation between hypo\/hypertelorism and normal fetuses based on MRI using automatic ocular biometric measurements, ocular ratios, and machine learning multi-parametric classification"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0719-1327","authenticated-orcid":false,"given":"Netanell","family":"Avisdris","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daphna","family":"Link Sourani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liat","family":"Ben-Sira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Joskowicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustavo","family":"Malinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simcha","family":"Yagel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elka","family":"Miller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dafna","family":"Ben Bashat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,7,12]]},"reference":[{"key":"8976_CR1","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1016\/j.survophthal.2018.04.006","volume":"63","author":"CL Ondeck","year":"2018","unstructured":"Ondeck CL, Pretorius D, McCaulley J et al (2018) Ultrasonographic prenatal imaging of fetal ocular and orbital abnormalities. Surv Ophthalmol 63:745\u2013753","journal-title":"Surv Ophthalmol"},{"key":"8976_CR2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-36079-5","volume-title":"Fetal and neonatal eye pathology","author":"RM Verdijk","year":"2020","unstructured":"Verdijk RM, Herwig-Carl MC (2020) Fetal and neonatal eye pathology. Springer Nature"},{"key":"8976_CR3","doi-asserted-by":"publisher","first-page":"1064","DOI":"10.1002\/pd.2612","volume":"30","author":"XB Li","year":"2010","unstructured":"Li XB, Kasprian G, Hodge JC et al (2010) Fetal ocular measurements by MRI. Prenat Diagn 30:1064\u20131071","journal-title":"Prenat Diagn"},{"key":"8976_CR4","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/0002-9378(82)90581-6","volume":"144","author":"KL Mayden","year":"1982","unstructured":"Mayden KL, Tortora M, Berkowitz RL, Bracken M, Hobbins JC (1982) Orbital diameters: a new parameter for prenatal diagnosis and dating. Am J Obstet Gynecol 144:289\u2013297","journal-title":"Am J Obstet Gynecol"},{"key":"8976_CR5","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s00330-018-5508-x","volume":"29","author":"R Batty","year":"2019","unstructured":"Batty R, Gawne-Cain ML, Mooney C et al (2019) Analysis of errors made on in utero MR studies of the foetal brain in the MERIDIAN study. Eur Radiol 29:195\u2013201","journal-title":"Eur Radiol"},{"key":"8976_CR6","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1007\/s00330-018-5695-5","volume":"29","author":"L Joskowicz","year":"2019","unstructured":"Joskowicz L, Cohen D, Caplan N, Sosna J (2019) Inter-observer variability of manual contour delineation of structures in CT. Eur Radiol 29:1391\u20131399","journal-title":"Eur Radiol"},{"key":"8976_CR7","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1007\/s00247-008-0929-3","volume":"38","author":"AJ Robinson","year":"2008","unstructured":"Robinson AJ, Blaser S, Toi A et al (2008) MRI of the fetal eyes: morphologic and biometric assessment for abnormal development with ultrasonographic and clinicopathologic correlation. Pediatr Radiol 38:971\u2013981","journal-title":"Pediatr Radiol"},{"key":"8976_CR8","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1002\/pd.4558","volume":"35","author":"C Velasco-Annis","year":"2015","unstructured":"Velasco-Annis C, Gholipour A, Afacan O, Prabhu SP, Estroff JA, Warfield SK (2015) Normative biometrics for fetal ocular growth using volumetric MRI reconstruction. Prenat Diagn 35:400\u2013408","journal-title":"Prenat Diagn"},{"key":"8976_CR9","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1148\/radiology.143.2.6803295","volume":"143","author":"P Jeanty","year":"1982","unstructured":"Jeanty P, Dramaix-Wilmet M, Van Gansbeke D, Van Regemorter N, Rodesch F (1982) Fetal ocular biometry by ultrasound. Radiology 143:513\u2013516","journal-title":"Radiology"},{"key":"8976_CR10","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1002\/pd.2274","volume":"29","author":"M Odeh","year":"2009","unstructured":"Odeh M, Feldman Y, Degani S, Grinin V, Ophir E, Bornstein J (2009) Fetal eyeball volume: relationship to gestational age and biparietal diameter. Prenat Diagn 29:749\u2013752","journal-title":"Prenat Diagn"},{"key":"8976_CR11","doi-asserted-by":"publisher","first-page":"W797","DOI":"10.2214\/AJR.13.10949","volume":"201","author":"NS Burns","year":"2013","unstructured":"Burns NS, Iyer RS, Robinson AJ, Chapman T (2013) Diagnostic imaging of fetal and pediatric orbital abnormalities. Am J Roentgenol 201:W797\u2013W808","journal-title":"Am J Roentgenol"},{"key":"8976_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","volume":"42","author":"G Litjens","year":"2017","unstructured":"Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60\u201388","journal-title":"Med Image Anal"},{"key":"8976_CR13","doi-asserted-by":"crossref","unstructured":"Dudovitch G, Link-Sourani D, Sira LB, Miller E, Bashat DB, Joskowicz L (2020) Deep learning automatic fetal structures segmentation in MRI scans with few annotated Datasets. In: Proc International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 365\u2013374","DOI":"10.1007\/978-3-030-59725-2_35"},{"key":"8976_CR14","doi-asserted-by":"crossref","unstructured":"Ebner M, Wang G, Li W et al (2018) An automated localization, segmentation and reconstruction framework for fetal brain MRI. In: Proc International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, pp 313\u2013320","DOI":"10.1007\/978-3-030-00928-1_36"},{"key":"8976_CR15","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.media.2018.10.003","volume":"51","author":"J Torrents-Barrena","year":"2019","unstructured":"Torrents-Barrena J, Piella G, Masoller N et al (2019) Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal 51:61\u201388","journal-title":"Med Image Anal"},{"key":"8976_CR16","doi-asserted-by":"publisher","first-page":"3775","DOI":"10.1007\/s00330-021-07915-9","volume":"31","author":"Y Kojita","year":"2021","unstructured":"Kojita Y, Matsuo H, Kanda T et al (2021) Deep learning model for predicting gestational age after the first trimester using fetal MRI. Eur Radiol 31:3775\u20133782","journal-title":"Eur Radiol"},{"key":"8976_CR17","doi-asserted-by":"crossref","unstructured":"Avisdris N, Ben Bashat D, Ben-Sira L, Joskowicz L (2021) Fetal brain MRI measurements using a deep learning landmark network with reliability estimation. In: uncertainty for safe utilization of machine learning in medical imaging, and perinatal imaging, placental and preterm image analysis. Springer, pp 210\u2013220","DOI":"10.1007\/978-3-030-87735-4_20"},{"key":"8976_CR18","first-page":"168","volume-title":"Spatio-temporal atlas of normal fetal craniofacial feature development and CNN-based ocular biometry for motion-corrected fetal MRI","author":"A Uus","year":"2021","unstructured":"Uus A, Matthew J, Grigorescu I et al (2021) Spatio-temporal atlas of normal fetal craniofacial feature development and CNN-based ocular biometry for motion-corrected fetal MRI. Springer International Publishing, Cham, pp 168\u2013178"},{"key":"8976_CR19","doi-asserted-by":"publisher","first-page":"1481","DOI":"10.1007\/s11548-021-02436-8","volume":"16","author":"N Avisdris","year":"2021","unstructured":"Avisdris N, Yehuda B, Ben-Zvi O et al (2021) Automatic linear measurements of the fetal brain on MRI with deep neural networks. Int J Comput Assist Radiol Surg 16:1481\u20131492","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"8976_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Proc International Conference on Medical image computing and computer-assisted intervention. Springer, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"8976_CR21","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proc IEEE conference on Computer Vision and Pattern Recognition, pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"8976_CR22","doi-asserted-by":"crossref","unstructured":"Sudre CH, Li W, Vercauteren T, Ourselin S, Cardoso MJ (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 240\u2013248","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"8976_CR23","unstructured":"Smith LN (2018) A disciplined approach to neural network hyper-parameters: Part 1--learning rate, batch size, momentum, and weight decay. arXiv preprint arXiv:180309820"},{"key":"8976_CR24","doi-asserted-by":"crossref","unstructured":"Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: Proc IEEE conference on computer vision and pattern recognition. IEEE, pp 248\u2013255","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"8976_CR25","doi-asserted-by":"crossref","unstructured":"Frid-Adar M, Ben-Cohen A, Amer R, Greenspan H (2018) Improving the segmentation of anatomical structures in chest radiographs using u-net with an imagenet pre-trained encoder. In: Image Analysis for Moving Organ, Breast, and Thoracic Images. Springer, pp 159\u2013168","DOI":"10.1007\/978-3-030-00946-5_17"},{"key":"8976_CR26","doi-asserted-by":"crossref","unstructured":"Howard J, Gugger S (2020) fastai: a layered API for deep learning. Information 11:108","DOI":"10.3390\/info11020108"},{"key":"8976_CR27","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","volume":"327","author":"JM Bland","year":"1986","unstructured":"Bland JM, Altman D (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327:307\u2013310","journal-title":"Lancet"},{"key":"8976_CR28","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y Benjamini","year":"1995","unstructured":"Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289\u2013300","journal-title":"J R Stat Soc Ser B Methodol"},{"key":"8976_CR29","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"8976_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1961189.1961199","volume":"2","author":"C-C Chang","year":"2011","unstructured":"Chang C-C, Lin C-J (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol (TIST) 2:1\u201327","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"8976_CR31","unstructured":"Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H (2015) Xgboost: extreme gradient boosting. R package version 04-2 1:1-4"},{"key":"8976_CR32","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321\u2013357","journal-title":"J Artif Intell Res"},{"key":"8976_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12864-019-6413-7","volume":"21","author":"D Chicco","year":"2020","unstructured":"Chicco D, Jurman G (2020) The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics 21:1\u201313","journal-title":"BMC Genomics"},{"key":"8976_CR34","unstructured":"Lundberg SM, Lee S-I (2017) A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems, pp 4768\u20134777"},{"key":"8976_CR35","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1002\/uog.8831","volume":"37","author":"LJ Salomon","year":"2011","unstructured":"Salomon LJ, Alfirevic Z, Berghella V et al (2011) Practice guidelines for performance of the routine mid-trimester fetal ultrasound scan. Ultrasound Obstet Gynecol 37:116\u2013126","journal-title":"Ultrasound Obstet Gynecol"},{"key":"8976_CR36","unstructured":"American College of Radiology (2019) ACR-ACOG-AIUM-SMFM-SRU practice parameter for the performance of standard diagnostic obstetrical ultrasound.\u00a0https:\/\/www.aium.org\/resources\/guidelines\/obstetric.pdf"},{"key":"8976_CR37","doi-asserted-by":"crossref","unstructured":"Whitehead MT, Vezina G (2016) Normal Developmental Globe Morphology on Fetal MR Imaging. AJNR Am J Neuroradiol 37:1733\u20131737","DOI":"10.3174\/ajnr.A4785"},{"key":"8976_CR38","doi-asserted-by":"publisher","first-page":"18","DOI":"10.4193\/Rhino12.111","volume":"51","author":"EP Prokopakis","year":"2013","unstructured":"Prokopakis EP, Vlastos IM, Picavet V et al (2013) The golden ratio in facial symmetry. Rhinology 51:18\u201321","journal-title":"Rhinology"},{"key":"8976_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s12045-017-0432-y","volume":"22","author":"S Abbas","year":"2017","unstructured":"Abbas S (2017) Golden ratio. Resonance 22:51\u201360","journal-title":"Resonance"},{"key":"8976_CR40","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1016\/j.media.2012.07.004","volume":"16","author":"M Kuklisova-Murgasova","year":"2012","unstructured":"Kuklisova-Murgasova M, Quaghebeur G, Rutherford MA, Hajnal JV, Schnabel JA (2012) Reconstruction of fetal brain MRI with intensity matching and complete outlier removal. Med Image Anal 16:1550\u20131564","journal-title":"Med Image Anal"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-022-08976-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-022-08976-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-022-08976-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,29]],"date-time":"2024-09-29T00:00:31Z","timestamp":1727568031000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-022-08976-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,12]]},"references-count":40,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["8976"],"URL":"https:\/\/doi.org\/10.1007\/s00330-022-08976-0","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,12]]},"assertion":[{"value":"28 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is Prof. Dafna Ben Bashat.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 retrospective\u2022 diagnostic or prognostic study\u2022 performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}