{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T22:19:14Z","timestamp":1781561954423,"version":"3.54.5"},"reference-count":45,"publisher":"IOP Publishing","issue":"22","license":[{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2023,11,6]],"date-time":"2023-11-06T00:00:00Z","timestamp":1699228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"name":"Dalian Engineering Research Center for Artificial Intelligence in Medical Imaging"},{"name":"Hainan Province Key Research and Development Plan","award":["ZDYF2021SHFZ244"],"award-info":[{"award-number":["ZDYF2021SHFZ244"]}]},{"name":"National Key Research and Development Program of China","award":["2020YFB1711500"],"award-info":[{"award-number":["2020YFB1711500"]}]},{"name":"Liaoning Key Lab of IC & BME System"},{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"crossref","award":["ZYYC21004"],"award-info":[{"award-number":["ZYYC21004"]}],"id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100013365","name":"West China Hospital","doi-asserted-by":"crossref","award":["1-3-5 project for disciplines of excellence"],"award-info":[{"award-number":["1-3-5 project for disciplines of excellence"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Fund of China","doi-asserted-by":"crossref","award":["81971693"],"award-info":[{"award-number":["81971693"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["DUT22YG229"],"award-info":[{"award-number":["DUT22YG229"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2023,11,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    <jats:italic>Objective.<\/jats:italic>\n                    Precise hip joint morphometry measurement from CT images is crucial for successful preoperative arthroplasty planning and biomechanical simulations. Although deep learning approaches have been applied to clinical bone surgery planning, there is still a lack of relevant research on quantifying hip joint morphometric parameters from CT images.\n                    <jats:italic>Approach.<\/jats:italic>\n                    This paper proposes a deep learning workflow for CT-based hip morphometry measurement. For the first step, a coarse-to-fine deep learning model is designed for accurate reconstruction of the hip geometry (3D bone models and key landmark points). Based on the geometric models, a robust measurement method is developed to calculate a full set of morphometric parameters, including the acetabular anteversion and inclination, the femoral neck shaft angle and the inclination, etc. Our methods were validated on two datasets with different imaging protocol parameters and further compared with the conventional 2D x-ray-based measurement method.\n                    <jats:italic>Main results<\/jats:italic>\n                    . The proposed method yields high bone segmentation accuracies (Dice coefficients of 98.18% and 97.85%, respectively) and low landmark prediction errors (1.55 mm and 1.65 mm) on both datasets. The automated measurements agree well with the radiologists\u2019 manual measurements (Pearson correlation coefficients between 0.47 and 0.99 and intraclass correlation coefficients between 0.46 and 0.98). This method provides more accurate measurements than the conventional 2D x-ray-based measurement method, reducing the error of acetabular cup size from over 2 mm to less than 1 mm. Moreover, our morphometry measurement method is robust against the error of the previous bone segmentation step. As we tested different deep learning methods for the prerequisite bone segmentation, our method produced consistent final measurement results, with only a 0.37 mm maximum inter-method difference in the cup size.\n                    <jats:italic>Significance<\/jats:italic>\n                    . This study proposes a deep learning approach with improved robustness and accuracy for pelvis arthroplasty planning.\n                  <\/jats:p>","DOI":"10.1088\/1361-6560\/ad04aa","type":"journal-article","created":{"date-parts":[[2023,10,18]],"date-time":"2023-10-18T18:45:05Z","timestamp":1697654705000},"page":"225003","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning-based workflow for hip joint morphometric parameter measurement from CT images"],"prefix":"10.1088","volume":"68","author":[{"given":"Haoyu","family":"Zhai","sequence":"first","affiliation":[{"name":"Dalian University of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Huang","sequence":"additional","affiliation":[{"name":"Army Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Li","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Dalian Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hairong","family":"Tao","sequence":"additional","affiliation":[{"name":"Shanghai Key Laboratory of Orthopaedic Implants"},{"name":"Shanghai Ninth People\u2019s Hospital"},{"name":"Shanghai Jiao Tong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinwu","family":"Wang","sequence":"additional","affiliation":[{"name":"Shanghai Ninth People\u2019s Hospital"},{"name":"Shanghai Jiaotong University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8136-9816","authenticated-orcid":false,"given":"Kang","family":"Li","sequence":"additional","affiliation":[{"name":"Sichuan University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moyu","family":"Shao","sequence":"additional","affiliation":[{"name":"Jiangsu Yunqianbai Digital Technology Co."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomin","family":"Cheng","sequence":"additional","affiliation":[{"name":"Jiangsu Yunqianbai Digital Technology Co."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Wang","sequence":"additional","affiliation":[{"name":"Xi\u2019an JiaoTong University. School of Chemical Engineering and Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Wu","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Xuzhou Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongkai","family":"Wang","sequence":"additional","affiliation":[{"name":"Dalian University of Technology"},{"name":"Liaoning Key Laboratory of Integrated Circuit and Biomedical Electronic System"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Xiong","sequence":"additional","affiliation":[{"name":"Army Medical University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2023,11,6]]},"reference":[{"key":"pmbad04aabib1","doi-asserted-by":"crossref","first-page":"179424","DOI":"10.1109\/ACCESS.2020.3026658","type":"journal-article","article-title":"VNet: an end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data","volume":"8","author":"Abdollahi","year":"2020","journal-title":"IEEE Access"},{"key":"pmbad04aabib2","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1109\/TMI.2003.812261","type":"journal-article","article-title":"Computational geometry for patient-specific reconstruction and meshing of blood vessels from MR and CT angiography","volume":"22","author":"Antiga","year":"2003","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad04aabib3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102417","type":"journal-article","article-title":"Multi-landmark environment analysis with reinforcement learning for pelvic abnormality detection and quantification","volume":"78","author":"Bekkouch","year":"2022","journal-title":"Med. Image Anal."},{"key":"pmbad04aabib4","doi-asserted-by":"publisher","DOI":"10.1016\/j.fri.2021.200436","type":"journal-article","article-title":"Announcement: the new mexico decedent image database","volume":"24","author":"Berry","year":"2021","journal-title":"Forensic Imaging"},{"key":"pmbad04aabib5","first-page":"392","type":"journal-article","article-title":"Measurement of lateral acetabular coverage: a comparison between CT and plain radiography","volume":"2","author":"Chadayammuri","year":"2015","journal-title":"J. Hip Preserv. Surg."},{"key":"pmbad04aabib6","doi-asserted-by":"publisher","first-page":"1152","DOI":"10.1111\/os.13298","type":"journal-article","article-title":"Validation of CT-based three-dimensional preoperative planning in comparison with acetate templating for primary total hip arthroplasty","volume":"14","author":"Chen","year":"2022","journal-title":"Orthopaedic Surg."},{"key":"pmbad04aabib7","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1007\/s10439-014-1176-4","type":"journal-article","article-title":"FACTS: fully automatic CT segmentation of a hip joint","volume":"43","author":"Chu","year":"2015","journal-title":"Ann. Biomed. Eng."},{"key":"pmbad04aabib8","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-319-46723-8_49","type":"book","article-title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","volume":"9901","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"pmbad04aabib9","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1302\/2058-5241.4.180075","type":"journal-article","article-title":"Total hip arthroplasty planning","volume":"4","author":"Colombi","year":"2019","journal-title":"EFORT Open Revi."},{"key":"pmbad04aabib10","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s11517-022-02529-9","type":"journal-article","article-title":"A deep learning-based approach to automatic proximal femur segmentation in quantitative CT images","volume":"60","author":"Deng","year":"2022","journal-title":"Med. Biol. Eng. Comput."},{"key":"pmbad04aabib11","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.otsr.2015.11.004","type":"journal-article","article-title":"Side-to-side variation in normal femoral morphology: 3D CT analysis of 122 femurs","volume":"102","author":"Dimitriou","year":"2016","journal-title":"Orthopaedics Traumatology: Surg. Res."},{"key":"pmbad04aabib12","article-title":"Anatomy, bony pelvis and lower limb, hip joint","author":"Gold","year":"2023","type":"book"},{"key":"pmbad04aabib13","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1007\/s11999-014-3932-z","type":"journal-article","article-title":"Sex-specific differences of the infraacetabular corridor: a biomorphometric CT-based analysis on a database of 523 pelves","volume":"473","author":"Gras","year":"2015","journal-title":"Clin. Orthop. Relat. Res."},{"key":"pmbad04aabib14","doi-asserted-by":"publisher","first-page":"1162","DOI":"10.1007\/s10439-013-0762-1","type":"journal-article","article-title":"Three-dimensional quantification of femoral head shape in controls and patients with cam-type femoroacetabular impingement","volume":"41","author":"Harris","year":"2013","journal-title":"Ann. Biomed. Eng."},{"key":"pmbad04aabib15","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1007\/978-3-031-08999-2_22","type":"book","article-title":"Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images","volume":"12962","author":"Hatamizadeh","year":"2022"},{"key":"pmbad04aabib16","doi-asserted-by":"publisher","first-page":"1774","DOI":"10.3390\/diagnostics12081774","type":"journal-article","article-title":"Morphological parameters of the hip joint and its relation to gender, joint side and age\u2014a CT-based study","volume":"12","author":"Hawellek","year":"2022","journal-title":"Diagnostics"},{"key":"pmbad04aabib17","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/s11548-019-02095-w","type":"journal-article","article-title":"An approach to automated measuring morphological parameters of proximal femora on three-dimensional models","volume":"15","author":"Hu","year":"2020","journal-title":"Int. J. CARS"},{"key":"pmbad04aabib18","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.4926654","type":"software","author":"Jaketmp","year":"2021","unstructured":"Jaketmp Tirrell L 2021 jaketmp\/pyCompare: (v1.5.2) Zenodo. https:\/\/doi.org\/10.5281\/zenodo.4926654"},{"key":"pmbad04aabib19","doi-asserted-by":"publisher","first-page":"S400\u2013407.e1","DOI":"10.1016\/j.arth.2022.03.033","type":"journal-article","article-title":"John charnley award: deep learning prediction of hip joint center on standard pelvis radiographs","volume":"37","author":"Jang","year":"2022","journal-title":"J. Arthroplasty"},{"key":"pmbad04aabib20","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1109\/TMI.2016.2578680","type":"journal-article","article-title":"Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks","volume":"35","author":"Jimenez-del-Toro","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"pmbad04aabib21","article-title":"CNN-based Segmentation of Medical Imaging Data","author":"Kayalibay","year":"2017","type":"preprint"},{"key":"pmbad04aabib22","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.1007\/s11999-014-4055-2","type":"journal-article","article-title":"Are normal hips being labeled as pathologic? a CT-based method for defining normal acetabular coverage","volume":"473","author":"Larson","year":"2015","journal-title":"Clin. Orthop. Relat. Res."},{"key":"pmbad04aabib23","doi-asserted-by":"publisher","first-page":"11354","DOI":"10.1609\/aaai.v34i07.6797","type":"journal-article","article-title":"Simple pose: rethinking and improving a bottom-up approach for multi-person pose estimation","volume":"34","author":"Li","year":"2020","journal-title":"AAAI"},{"key":"pmbad04aabib24","first-page":"3063","type":"other","article-title":"Structure regularized attentive network for automatic femoral head necrosis diagnosis and localization","author":"Li","year":"2022"},{"key":"pmbad04aabib25","first-page":"670","type":"conference-proceedings","article-title":"Landmarks detection with anatomical constraints for total hip arthroplasty preoperative measurements","author":"Liu","year":"2020"},{"key":"pmbad04aabib26","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1145\/37402.37422","type":"journal-article","article-title":"Marching cubes: a high resolution 3D surface construction algorithm","volume":"21","author":"Lorensen","year":"1987","journal-title":"SIGGRAPH Comput. Graph."},{"key":"pmbad04aabib27","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1097\/00003086-200112000-00006","type":"journal-article","article-title":"Morphologic features of the acetabulum and femur: anteversion angle and implant positioning","volume":"393","author":"Maruyama","year":"2001","journal-title":"Clin. Orthop. Relat. Res."},{"key":"pmbad04aabib28","doi-asserted-by":"publisher","first-page":"1714","DOI":"10.1359\/jbmr.1999.14.10.1714","type":"journal-article","article-title":"Femoral neck length and hip fracture risk","volume":"14","author":"Michelotti","year":"1999","journal-title":"J. Bone Miner. Res."},{"key":"pmbad04aabib29","doi-asserted-by":"publisher","first-page":"2846","DOI":"10.1007\/s11999-011-1958-z","type":"journal-article","article-title":"A quantitative method to assess focal acetabular overcoverage resulting from pincer deformity using CT data","volume":"469","author":"Murphy","year":"2011","journal-title":"Clin. Orthopaedics Related Res."},{"key":"pmbad04aabib30","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1302\/0301-620X.75B2.8444942","type":"journal-article","article-title":"The definition and measurement of acetabular orientation","volume":"75-B","author":"Murray","year":"1993","journal-title":"The J. Bone Joint Surg. Br. Volume"},{"key":"pmbad04aabib31","first-page":"632","type":"conference-proceedings","article-title":"3D Slicer","volume":"1","author":"Pieper","year":"2004"},{"key":"pmbad04aabib32","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1016\/S0035-1040(07)90327-X","type":"journal-article","article-title":"Low accuracy of the anterior pelvic plane to guide the position of the cup with imageless computer assistance: variation of position in 106 patients","volume":"93","author":"Pinoit","year":"2007","journal-title":"Rev. Chir. Orthop. Reparatrice Appar. Mot."},{"key":"pmbad04aabib33","doi-asserted-by":"publisher","first-page":"2228\u20132234.e1","DOI":"10.1016\/j.arth.2019.04.055","type":"journal-article","article-title":"Preoperative prediction of value metrics and a patient-specific payment model for primary total hip arthroplasty: development and validation of a deep learning model","volume":"34","author":"Ramkumar","year":"2019","journal-title":"J. Arthroplasty"},{"key":"pmbad04aabib34","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","type":"book","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"9351","author":"Ronneberger","year":"2015"},{"key":"pmbad04aabib35","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1177\/1120700018776311","type":"journal-article","article-title":"Is computerised 3D templating more accurate than 2D templating to predict size of components in primary total hip arthroplasty?","volume":"29","author":"Schiffner","year":"2019","journal-title":"HIP Int."},{"key":"pmbad04aabib36","first-page":"538","type":"conference-proceedings","article-title":"Automatic extraction of anatomical landmarks from medical image data: an evaluation of different methods","author":"Seim","year":"2009"},{"key":"pmbad04aabib37","doi-asserted-by":"publisher","first-page":"811","DOI":"10.1007\/s00256-011-1293-1","type":"journal-article","article-title":"Reliability of radiographic parameters in adults with hip dysplasia","volume":"41","author":"Terjesen","year":"2012","journal-title":"Skeletal Radiol."},{"key":"pmbad04aabib38","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.arth.2017.09.027","type":"journal-article","article-title":"Can anatomic measurements of stem anteversion angle be considered as the functional anteversion angle?","volume":"33","author":"Uemura","year":"2018","journal-title":"J. Arthroplasty"},{"key":"pmbad04aabib39","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109303","type":"journal-article","article-title":"Feasibility of automatic measurements of hip joints based on pelvic radiography and a deep learning algorithm","volume":"132","author":"Yang","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"pmbad04aabib40","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1007\/978-3-642-40763-5_24","type":"book","article-title":"Automated CT segmentation of diseased hip using hierarchical and conditional statistical shape models","volume":"7908","author":"Yokota","year":"2013"},{"key":"pmbad04aabib41","doi-asserted-by":"crossref","DOI":"10.1177\/23259671211046916","type":"journal-article","article-title":"Three-dimensional magnetic resonance imaging bone models of the hip joint using deep learning: dynamic simulation of hip impingement for diagnosis of intra- and extra-articular hip impingement","volume":"9","author":"Zeng","year":"2021a","journal-title":"Orthopaedic J. Sports Med."},{"key":"pmbad04aabib42","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejro.2020.100303","type":"journal-article","article-title":"MRI-based 3D models of the hip joint enables radiation-free computer-assisted planning of periacetabular osteotomy for treatment of hip dysplasia using deep learning for automatic segmentation","volume":"8","author":"Zeng","year":"2021b","journal-title":"Eur. J. Radiol Open"},{"key":"pmbad04aabib43","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0172297","type":"journal-article","article-title":"Three-dimensional acetabular orientation measurement in a reliable coordinate system among one hundred Chinese","volume":"12","author":"Zhang","year":"2017","journal-title":"PLoS One"},{"key":"pmbad04aabib44","doi-asserted-by":"publisher","first-page":"1107","DOI":"10.1007\/s00276-020-02676-4","type":"journal-article","article-title":"Morphological consistency of bilateral hip joints in adults based on the x-ray and CT data","volume":"43","author":"Zhao","year":"2021","journal-title":"Surg. Radiol. Anat."},{"key":"pmbad04aabib45","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1109\/JPROC.2021.3054390","type":"journal-article","article-title":"A review of deep learning in medical imaging: imaging traits, technology trends, case studies with progress highlights, and future promises","volume":"109","author":"Zhou","year":"2021","journal-title":"Proc. IEEE"}],"container-title":["Physics in Medicine &amp; Biology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T16:46:08Z","timestamp":1778777168000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/1361-6560\/ad04aa"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,6]]},"references-count":45,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2023,11,6]]},"published-print":{"date-parts":[[2023,11,21]]}},"URL":"https:\/\/doi.org\/10.1088\/1361-6560\/ad04aa","relation":{},"ISSN":["0031-9155","1361-6560"],"issn-type":[{"value":"0031-9155","type":"print"},{"value":"1361-6560","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,6]]},"assertion":[{"value":"Deep learning-based workflow for hip joint morphometric parameter measurement from CT images","name":"article_title","label":"Article Title"},{"value":"Physics in Medicine & Biology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2023 Institute of Physics and Engineering in Medicine. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2023-05-07","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-10-18","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2023-11-06","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}