{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:07:04Z","timestamp":1792598824102,"version":"4.5.0"},"reference-count":24,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2019,1,31]],"date-time":"2019-01-31T00:00:00Z","timestamp":1548892800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Swedish Research Council, Region Sk\u00e5ne, Knut and Alice Wallenberg Foundation and EXINI Diagnostics AB"}],"content-domain":{"domain":["ejradiology.com","clinicalkey.jp","clinicalkey.com","clinicalkey.es","clinicalkey.com.au","clinicalkey.fr","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["European Journal of Radiology"],"published-print":{"date-parts":[[2019,4]]},"DOI":"10.1016\/j.ejrad.2019.01.028","type":"journal-article","created":{"date-parts":[[2019,2,1]],"date-time":"2019-02-01T06:04:20Z","timestamp":1549001060000},"page":"89-95","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":118,"special_numbering":"C","title":["Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET\/CT-based 3D quantification of skeletal metastases"],"prefix":"10.1016","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2792-9402","authenticated-orcid":false,"given":"Sarah","family":"Lindgren Belal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"May","family":"Sadik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reza","family":"Kaboteh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olof","family":"Enqvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Ul\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mads H.","family":"Poulsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane","family":"Simonsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Poul F.","family":"H\u00f8ilund-Carlsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Edenbrandt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elin","family":"Tr\u00e4g\u00e5rdh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"12","key":"10.1016\/j.ejrad.2019.01.028_bib0005","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1200\/JCO.2015.64.2702","article-title":"Trial design and objectives for castration-resistant prostate Cancer: updated recommendations from the prostate Cancer Clinical trials working group 3","volume":"34","author":"Scher","year":"2016","journal-title":"J. Clin. Oncol."},{"issue":"8","key":"10.1016\/j.ejrad.2019.01.028_bib0010","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1007\/s00259-008-0721-5","article-title":"Quality of planar whole-body bone scan interpretations--a nationwide survey","volume":"35","author":"Sadik","year":"2008","journal-title":"Eur. J. Nucl. Med. Mol. Imagning"},{"issue":"7","key":"10.1016\/j.ejrad.2019.01.028_bib0015","first-page":"1765","article-title":"A new parameter for measuring metastatic bone involvement by prostate cancer: the Bone Scan Index","volume":"4","author":"Imbriaco","year":"1998","journal-title":"Clin. Cancer Res."},{"issue":"1","key":"10.1016\/j.ejrad.2019.01.028_bib0020","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.eururo.2012.01.037","article-title":"A novel automated platform for quantifying the extent of skeletal tumour involvement in prostate cancer patients using the Bone Scan Index","volume":"26","author":"Ulmert","year":"2012","journal-title":"Eur. Urol."},{"issue":"48","key":"10.1016\/j.ejrad.2019.01.028_bib0025","doi-asserted-by":"crossref","first-page":"84449","DOI":"10.18632\/oncotarget.19680","article-title":"Prognostic value of bone scan index as an imaging biomarker in metastatic prostate cancer: a meta-analysis","volume":"8","author":"Li","year":"2017","journal-title":"Oncotarget"},{"issue":"1","key":"10.1016\/j.ejrad.2019.01.028_bib0030","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4103\/0971-6203.58777","article-title":"Automated medical image segmentation techniques","volume":"35","author":"Sharma","year":"2010","journal-title":"J. Med. Phys."},{"issue":"2","key":"10.1016\/j.ejrad.2019.01.028_bib0035","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"10.1016\/j.ejrad.2019.01.028_bib0040","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.media.2017.05.001","article-title":"3D deeply supervised network for automated segmentation of volumetric medical images","volume":"41","author":"Dou","year":"2017","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/j.ejrad.2019.01.028_bib0045","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1111\/bju.12599","article-title":"Spine metastases in prostate cancer: comparison of technetium-99m-MDP whole-body bone scintigraphy, [(18) F]choline positron emission tomography(PET)\/computed tomography (CT) and [(18) F]NaF PET\/CT","volume":"114","author":"Poulsen","year":"2014","journal-title":"BJU Int."},{"issue":"1","key":"10.1016\/j.ejrad.2019.01.028_bib0050","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s13550-017-0264-5","article-title":"3D skeletal uptake of 18F sodium fluoride in PET\/CT images is associated with overall survival in patients with prostate cancer","volume":"7","author":"Lindgren Belal","year":"2017","journal-title":"EJNMMI Res."},{"key":"10.1016\/j.ejrad.2019.01.028_bib0055","series-title":"Courville A. Deep Learning","author":"Goodfellow","year":"2016"},{"key":"10.1016\/j.ejrad.2019.01.028_bib0060","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition: IEEE"},{"key":"10.1016\/j.ejrad.2019.01.028_bib0065","series-title":"European Conference on Computer Vision","article-title":"Microsoft COCO: Common objects in context","author":"Lin","year":"2014"},{"issue":"2","key":"10.1016\/j.ejrad.2019.01.028_bib0070","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The PASCAL visual object classes (VOC) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.ejrad.2019.01.028_bib0075","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"The International Conference on Learning Representations: arXiv.Org"},{"key":"10.1016\/j.ejrad.2019.01.028_bib0080","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecological association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology."},{"issue":"2","key":"10.1016\/j.ejrad.2019.01.028_bib0085","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1177\/096228029900800204","article-title":"Measuring agreement in method comparison studies","volume":"8","author":"Bland","year":"1999","journal-title":"Stat. Methods Med. Res."},{"issue":"3","key":"10.1016\/j.ejrad.2019.01.028_bib0090","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1148\/radiol.2017171183","article-title":"When machines think: radiology\u2019s next frontier","volume":"285","author":"Dreyer","year":"2017","journal-title":"Radiology"},{"issue":"10","key":"10.1016\/j.ejrad.2019.01.028_bib0095","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.2967\/jnumed.115.156026","article-title":"Determination of skeletal tumor burden on 18F-Fluoride PET\/CT","volume":"56","author":"Rohren","year":"2015","journal-title":"J. Nucl. Med."},{"issue":"8","key":"10.1016\/j.ejrad.2019.01.028_bib0100","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.2967\/jnumed.115.158626","article-title":"Prognostic factors in patients treated with 223Ra: the role of skeletal tumor burden on baseline 18F-Fluoride PET\/CT in predicting overall survival","volume":"56","author":"Etchebehere","year":"2015","journal-title":"J. Nucl. Med."},{"issue":"24","key":"10.1016\/j.ejrad.2019.01.028_bib0105","doi-asserted-by":"crossref","first-page":"2829","DOI":"10.1200\/JCO.2017.72.2348","article-title":"Quantitative assessment of early [(18)F]Sodium fluoride positron emission Tomography\/Computed tomography response to treatment in men with metastatic prostate Cancer to bone","volume":"35","author":"Harmon","year":"2017","journal-title":"J. Clin. Oncol."},{"key":"10.1016\/j.ejrad.2019.01.028_bib0110","series-title":"American Cancer Society","year":"2019"},{"issue":"1","key":"10.1016\/j.ejrad.2019.01.028_bib0115","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1097\/RTI.0000000000000311","article-title":"Reinventing radiology: big data and the future of medical imaging","volume":"33","author":"Morris","year":"2018","journal-title":"J. Thorac. Imaging"},{"issue":"4","key":"10.1016\/j.ejrad.2019.01.028_bib0120","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1097\/MNM.0b013e3283504528","article-title":"Study on the distribution features of bone metastases in prostate cancer","volume":"33","author":"Wang","year":"2012","journal-title":"Nucl. Med. Commun."}],"container-title":["European Journal of Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X1930035X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0720048X1930035X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T22:09:15Z","timestamp":1553724555000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0720048X1930035X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4]]},"references-count":24,"alternative-id":["S0720048X1930035X"],"URL":"https:\/\/doi.org\/10.1016\/j.ejrad.2019.01.028","relation":{},"ISSN":["0720-048X"],"issn-type":[{"value":"0720-048X","type":"print"}],"subject":[],"published":{"date-parts":[[2019,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep learning for segmentation of 49 selected bones in CT scans: First step in automated PET\/CT-based 3D quantification of skeletal metastases","name":"articletitle","label":"Article Title"},{"value":"European Journal of Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ejrad.2019.01.028","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}]}}