{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T15:12:45Z","timestamp":1766848365124,"version":"3.37.3"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"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":["Appl Intell"],"published-print":{"date-parts":[[2024,3]]},"DOI":"10.1007\/s10489-024-05406-0","type":"journal-article","created":{"date-parts":[[2024,3,26]],"date-time":"2024-03-26T07:10:30Z","timestamp":1711437030000},"page":"4319-4333","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Generative adversarial network for newborn 3D skeleton part segmentation"],"prefix":"10.1007","volume":"54","author":[{"given":"Hien-Duyen","family":"Nguyen-Le","sequence":"first","affiliation":[]},{"given":"Morgane","family":"Ferrandini","sequence":"additional","affiliation":[]},{"given":"Duc-Phong","family":"Nguyen","sequence":"additional","affiliation":[]},{"given":"Vi-Do","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Hoai-Danh","family":"Vo","sequence":"additional","affiliation":[]},{"given":"Tan-Nhu","family":"Nguyen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5088-3433","authenticated-orcid":false,"given":"Tien-Tuan","family":"Dao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,26]]},"reference":[{"key":"5406_CR1","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1093\/pch\/pxab083","volume":"26","author":"V Shah","year":"2021","unstructured":"Shah V, Coroneos CJ, Ng E (2021) The evaluation and management of neonatal brachial plexus palsy. Paediatr Child Health 26:493\u2013497. https:\/\/doi.org\/10.1093\/pch\/pxab083","journal-title":"Paediatr Child Health"},{"key":"5406_CR2","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/S0029-7844(03)00483-6","volume":"102","author":"CR Macedonia","year":"2003","unstructured":"Macedonia CR, Gherman RB, Satin AJ (2003) Simulation laboratories for training in obstetrics and gynecology. Obstet Gynecol 102:388\u2013392. https:\/\/doi.org\/10.1016\/S0029-7844(03)00483-6","journal-title":"Obstet Gynecol"},{"key":"5406_CR3","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.ajog.2004.09.028","volume":"192","author":"O Dupuis","year":"2005","unstructured":"Dupuis O, Silveira R, Zentner A, Dittmar A, Gaucherand P, Cucherat M, Redarce T, Rudigoz R-C (2005) Birth simulator: reliability of transvaginal assessment of fetal head station as defined by the American College of Obstetricians and Gynecologists classification. Am J Obstet Gynecol 192:868\u2013874. https:\/\/doi.org\/10.1016\/j.ajog.2004.09.028","journal-title":"Am J Obstet Gynecol"},{"key":"5406_CR4","doi-asserted-by":"publisher","first-page":"S166","DOI":"10.1016\/j.ejogrb.2009.02.033","volume":"144","author":"MPL Parente","year":"2009","unstructured":"Parente MPL, Jorge RMN, Mascarenhas T, Fernandes AA, Martins JAC (2009) The influence of an occipito-posterior malposition on the biomechanical behavior of the pelvic floor. Eur J Obstet Gynecol Reprod Biol 144:S166\u2013S169. https:\/\/doi.org\/10.1016\/j.ejogrb.2009.02.033","journal-title":"Eur J Obstet Gynecol Reprod Biol"},{"key":"5406_CR5","doi-asserted-by":"publisher","first-page":"50801","DOI":"10.1115\/1.4049226","volume":"143","author":"S Chen","year":"2021","unstructured":"Chen S, Grimm MJ (2021) Childbirth computational models: characteristics and applications. J Biomech Eng 143:50801. https:\/\/doi.org\/10.1115\/1.4049226","journal-title":"J Biomech Eng"},{"key":"5406_CR6","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1007\/s10237-018-01109-x","volume":"18","author":"R Lapeer","year":"2019","unstructured":"Lapeer R, Gerikhanov Z, Sadulaev S-M, Audinis V, Rowland R, Crozier K, Morris E (2019) A computer-based simulation of childbirth using the partial Dirichlet-Neumann contact method with total Lagrangian explicit dynamics on the GPU. Biomech Model Mechanobiol 18:681\u2013700. https:\/\/doi.org\/10.1007\/s10237-018-01109-x","journal-title":"Biomech Model Mechanobiol"},{"key":"5406_CR7","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0215721","volume":"14","author":"O Ami","year":"2019","unstructured":"Ami O, Maran JC, Gabor P, Whitacre EB, Musset D, Dubray C, Mage G, Boyer L (2019) Three-dimensional magnetic resonance imaging of fetal head molding and brain shape changes during the second stage of labor. PLoS ONE 14:e0215721. https:\/\/doi.org\/10.1371\/journal.pone.0215721","journal-title":"PLoS ONE"},{"key":"5406_CR8","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1007\/s11517-018-1940-y","volume":"57","author":"TT Dao","year":"2019","unstructured":"Dao TT (2019) From deep learning to transfer learning for the prediction of skeletal muscle forces. Med Biol Eng Comput 57:1049\u20131058. https:\/\/doi.org\/10.1007\/s11517-018-1940-y","journal-title":"Med Biol Eng Comput"},{"key":"5406_CR9","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1007\/s11517-022-02541-z","volume":"60","author":"A Ballit","year":"2022","unstructured":"Ballit A, Dao T-T (2022) Recurrent neural network to predict hyperelastic constitutive behaviors of the skeletal muscle. Med Biol Eng Comput 60:1177. https:\/\/doi.org\/10.1007\/s11517-022-02541-z","journal-title":"Med Biol Eng Comput"},{"key":"5406_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107150","volume":"126","author":"DH Nguyen-Le","year":"2023","unstructured":"Nguyen-Le DH, Ballit A, Dao T-T (2023) A novel deep learning-driven approach for predicting the pelvis soft-tissue deformations toward a real-time interactive childbirth simulation. Eng Appl Artif Intell 126:107150. https:\/\/doi.org\/10.1016\/j.engappai.2023.107150","journal-title":"Eng Appl Artif Intell"},{"key":"5406_CR11","doi-asserted-by":"crossref","unstructured":"O\u2019Mahony N, Campbell S, Carvalho A , Harapanahalli S, Hernandez GV, Krpalkova L, Riordan D, Walsh J (2020) Deep learning vs. traditional computer vision. In: Arai K, Kapoor S (ed) Advances in Computer Vision. Springer International Publishing, Cham, pp 128\u2013144","DOI":"10.1007\/978-3-030-17795-9_10"},{"key":"5406_CR12","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.neunet.2021.03.004","volume":"140","author":"Z Bai","year":"2021","unstructured":"Bai Z, Zhang X-L (2021) Speaker recognition based on deep learning: an overview. Neural Netw 140:65\u201399. https:\/\/doi.org\/10.1016\/j.neunet.2021.03.004","journal-title":"Neural Netw"},{"key":"5406_CR13","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","volume":"25","author":"A Esteva","year":"2019","unstructured":"Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J (2019) A guide to deep learning in healthcare. Nat Med 25:24\u201329. https:\/\/doi.org\/10.1038\/s41591-018-0316-z","journal-title":"Nat Med"},{"key":"5406_CR14","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/s41591-018-0320-3","volume":"25","author":"B Norgeot","year":"2019","unstructured":"Norgeot B, Glicksberg BS, Butte AJ (2019) A call for deep-learning healthcare. Nat Med 25:14\u201315. https:\/\/doi.org\/10.1038\/s41591-018-0320-3","journal-title":"Nat Med"},{"key":"5406_CR15","first-page":"13","volume":"11","author":"B G\u00fcr\u00fcnl\u00fc","year":"2022","unstructured":"G\u00fcr\u00fcnl\u00fc B, \u00d6zt\u00fcrk S (2022) A novel method for forgery detection on lung cancer images. Int J Inform Secur Sci 11:13\u201320","journal-title":"Int J Inform Secur Sci"},{"key":"5406_CR16","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1007\/s11548-021-02363-8","volume":"16","author":"P Liu","year":"2021","unstructured":"Liu P, Han H, Du Y, Zhu H, Li Y, Gu F, Xiao H, Li J, Zhao C, Xiao L, Wu X, Zhou SK (2021) Deep learning to segment pelvic bones: large-scale CT datasets and baseline models. Int J Comput Assist Radiol Surg 16:749\u2013756. https:\/\/doi.org\/10.1007\/s11548-021-02363-8","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"5406_CR17","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1186\/s13244-021-01044-z","volume":"12","author":"X Liu","year":"2021","unstructured":"Liu X, Han C, Wang H, Wu J, Cui Y, Zhang X, Wang X (2021) Fully automated pelvic bone segmentation in multiparameteric MRI using a 3D convolutional neural network. Insights Imaging 12:93. https:\/\/doi.org\/10.1186\/s13244-021-01044-z","journal-title":"Insights Imaging"},{"key":"5406_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105798","volume":"200","author":"L Zhang","year":"2020","unstructured":"Zhang L, Wang H (2020) A novel segmentation method for cervical vertebrae based on PointNet\u2009+\u2009+\u2009and converge segmentation. Comput Methods Programs Biomed 200:105798. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105798","journal-title":"Comput Methods Programs Biomed"},{"key":"5406_CR19","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63:139\u2013144. https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun ACM"},{"key":"5406_CR20","unstructured":"Odena A, Olah C, Shlens J (2017) Conditional Image Synthesis with Auxiliary Classifier GANs. In: Precup D, Teh YW (eds) Proceedings of the 34th International Conference on Machine Learning. PMLR, pp 2642\u20132651"},{"key":"5406_CR21","doi-asserted-by":"crossref","unstructured":"Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp 2242\u20132251","DOI":"10.1109\/ICCV.2017.244"},{"key":"5406_CR22","unstructured":"Ma C, Yang Y, Guo J, Pan F, Wang C, Guo Y (2022) Unsupervised point cloud completion and segmentation by Generative Adversarial Autoencoding Network. In: Koyejo S, Mohamed S, Agarwal A, Belgrave D, Cho K, Oh A (eds) Advances in Neural Information Processing systems. Curran Associates, Inc, pp 3556\u20133568"},{"key":"5406_CR23","doi-asserted-by":"publisher","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017) PointNet: Deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 652\u2013660. https:\/\/doi.org\/10.1109\/CVPR.2017.16","DOI":"10.1109\/CVPR.2017.16"},{"key":"5406_CR24","doi-asserted-by":"crossref","unstructured":"Hua B, Tran M, Yeung S (2018) Pointwise Convolutional Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 984\u2013993","DOI":"10.1109\/CVPR.2018.00109"},{"key":"5406_CR25","doi-asserted-by":"publisher","unstructured":"Edgar HJH, Daneshvari Berry S, Moes E, Adolphi NL, Bridges P, Nolte KB (2020) New Mexico Decedent Image Database (NMDID). https:\/\/doi.org\/10.25827\/5S8C-N515","DOI":"10.25827\/5S8C-N515"},{"key":"5406_CR26","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","volume":"70","author":"A Garcia-Garcia","year":"2018","unstructured":"Garcia-Garcia A, Orts-Escolano S, Oprea S, Villena-Martinez V, Martinez-Gonzalez P, Garcia-Rodriguez J (2018) A survey on deep learning techniques for image and video semantic segmentation. Appl Soft Comput 70:41\u201365. https:\/\/doi.org\/10.1016\/j.asoc.2018.05.018","journal-title":"Appl Soft Comput"},{"key":"5406_CR27","doi-asserted-by":"publisher","unstructured":"Nguyen T-N-T, Ballit A, Lecomte-Grosbras P, Colliat J-B, Dao T-T (n.d.) The uncertainty quantification of hyperelastic properties using precise and imprecise probabilities toward reliable in silico simulation of the second-stage labor. J Mech Med Biol 0:2350083. https:\/\/doi.org\/10.1142\/S0219519423500835","DOI":"10.1142\/S0219519423500835"},{"key":"5406_CR28","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1007\/s11517-023-02864-5","volume":"61","author":"A Ballit","year":"2023","unstructured":"Ballit A, Hivert M, Rubod C, Dao T-T (2023) Fast soft-tissue deformations coupled with mixed reality toward the next-generation childbirth training simulator. Med Biol Eng Comput 61:2207\u20132226. https:\/\/doi.org\/10.1007\/s11517-023-02864-5","journal-title":"Med Biol Eng Comput"},{"key":"5406_CR29","unstructured":"Achlioptas P, Diamanti O, Mitliagkas I, Guibas L (2018) Learning representations and generative models for 3D point clouds. In: Dy J, Krause A (eds) Proceedings of the 35th International Conference on Machine Learning, PMLR, pp 40\u201349"},{"key":"5406_CR30","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1016\/j.neucom.2019.12.032","volume":"384","author":"Y Yu","year":"2020","unstructured":"Yu Y, Huang Z, Li F, Zhang H, Le X (2020) Point Encoder GAN: a deep learning model for 3D point cloud inpainting. Neurocomputing 384:192\u2013199. https:\/\/doi.org\/10.1016\/j.neucom.2019.12.032","journal-title":"Neurocomputing"},{"key":"5406_CR31","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1111\/cgf.14151","volume":"39","author":"H Qin","year":"2020","unstructured":"Qin H, Zhang S, Liu Q, Chen L, Chen B (2020) PointSkelCNN: deep learning-based 3D human skeleton extraction from point clouds. Comput Graphics Forum 39:363\u2013374. https:\/\/doi.org\/10.1111\/cgf.14151","journal-title":"Comput Graphics Forum"},{"key":"5406_CR32","doi-asserted-by":"crossref","unstructured":"Takmaz A, Schult J, Kaftan I, Ak\u00e7ay M, Leibe B, Sumner R, Engelmann F, Tang S (2023) 3D Segmentation of Humans in Point Clouds with Synthetic Data. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","DOI":"10.1109\/ICCV51070.2023.00125"},{"key":"5406_CR33","unstructured":"Qi CR, Yi L, Su H, Guibas LJ (2017) PointNet++: deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, pp 5105\u20135114"},{"key":"5406_CR34","doi-asserted-by":"publisher","first-page":"2919","DOI":"10.1109\/TVCG.2019.2896310","volume":"26","author":"Z Wang","year":"2018","unstructured":"Wang Z, Lu F (2018) VoxSegNet: volumetric CNNs for semantic part segmentation of 3D shapes. IEEE Trans Vis Comput Graph 26:2919\u20132930","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"5406_CR35","doi-asserted-by":"crossref","unstructured":"Yu F, Liu K, Zhang Y, Zhu C, Xu K (2019) PartNet: A Recursive Part Decomposition Network for Fine-Grained and Hierarchical Shape Segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9483\u20139492. IEEE Computer Society, Los Alamitos","DOI":"10.1109\/CVPR.2019.00972"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05406-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05406-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05406-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T13:42:17Z","timestamp":1714398137000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05406-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":35,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["5406"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05406-0","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2024,3]]},"assertion":[{"value":"15 March 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that there is no conflict of interest related to this present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}},{"value":"There is no ethical issue for the present study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval\u00a0and consent"}}]}}