{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,6]],"date-time":"2024-07-06T15:45:03Z","timestamp":1720280703282},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003625","name":"Ministry of Health and Welfare","doi-asserted-by":"publisher","award":["HI18C2383"],"id":[{"id":"10.13039\/501100003625","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003710","name":"Korea Health Industry Development Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2018R1C1B6006371"],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1016\/j.cmpb.2022.106627","type":"journal-article","created":{"date-parts":[[2022,1,10]],"date-time":"2022-01-10T10:02:05Z","timestamp":1641808925000},"page":"106627","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["Bone suppression on pediatric chest radiographs via a deep learning-based cascade model"],"prefix":"10.1016","volume":"215","author":[{"given":"Kyungjin","family":"Cho","sequence":"first","affiliation":[]},{"given":"Jiyeon","family":"Seo","sequence":"additional","affiliation":[]},{"given":"Sunggu","family":"Kyung","sequence":"additional","affiliation":[]},{"given":"Mingyu","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Gil-Sun","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Namkug","family":"Kim","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2022.106627_bib0001","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1148\/rg.261055034","article-title":"Dual-energy subtraction chest radiography: what to look for beyond calcified nodules","volume":"26","author":"Kuhlman","year":"2006","journal-title":"Radiographics"},{"key":"10.1016\/j.cmpb.2022.106627_bib0002","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TMI.2006.871549","article-title":"Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)","volume":"25","author":"Suzuki","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2022.106627_bib0003","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"678","article-title":"Generating Dual-Energy Subtraction Soft-Tissue Images from Chest Radiographs via Bone Edge-Guided GAN","author":"Liu","year":"2020"},{"key":"10.1016\/j.cmpb.2022.106627_bib0004","unstructured":"Goodfellow I.J., Pouget-Abadie J., Mirza M., Xu B., Warde-Farley D., Ozair S., et\u00a0al. Generative adversarial networks. arXiv preprint arXiv:14062661. 2014."},{"key":"10.1016\/j.cmpb.2022.106627_bib0005","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.compmedimag.2016.04.002","article-title":"Atlas-based rib-bone detection in chest X-rays","volume":"51","author":"Candemir","year":"2016","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.cmpb.2022.106627_bib0006","doi-asserted-by":"crossref","first-page":"19","DOI":"10.3311\/PPee.2079","article-title":"An X-ray CAD system with ribcage suppression for improved detection of lung lesions","volume":"57","author":"Horv\u00e1th","year":"2013","journal-title":"Periodica Polytechnica Electrical Eng."},{"key":"10.1016\/j.cmpb.2022.106627_bib0007","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1007\/s11548-015-1278-y","article-title":"A novel bone suppression method that improves lung nodule detection","volume":"4","author":"Berg","year":"2016","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.cmpb.2022.106627_bib0008","series-title":"2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)","first-page":"1362","article-title":"When does bone suppression and lung field segmentation improve chest x-ray disease classification?","author":"Baltruschat","year":"2019"},{"key":"10.1016\/j.cmpb.2022.106627_bib0009","doi-asserted-by":"crossref","first-page":"2232","DOI":"10.1002\/mp.13468","article-title":"Separation of bones from soft tissue in chest radiographs: anatomy-specific orientation-frequency-specific deep neural network convolution","volume":"46","author":"Zarshenas","year":"2019","journal-title":"Med. Phys."},{"key":"10.1016\/j.cmpb.2022.106627_bib0010","series-title":"4th European Conference of the International Federation for Medical and Biological Engineering","first-page":"488","article-title":"Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs","author":"Simk\u00f3","year":"2009"},{"key":"10.1016\/j.cmpb.2022.106627_bib0011","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.cmpb.2015.12.006","article-title":"Eliminating rib shadows in chest radiographic images providing diagnostic assistance","volume":"127","author":"O\u011ful","year":"2016","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.cmpb.2022.106627_bib0012","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1109\/TMI.2013.2274212","article-title":"Suppression of translucent elongated structures: applications in chest radiography","volume":"32","author":"Hogeweg","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2022.106627_bib0013","series-title":"2007 9th International Symposium on Signal Processing and Its Applications","first-page":"1","article-title":"Rib suppression in frontal chest radiographs: a blind source separation approach","author":"Rasheed","year":"2007"},{"key":"10.1016\/j.cmpb.2022.106627_bib0014","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1117\/12.480862","article-title":"Segmenting the posterior ribs in chest radiographs by iterated contextual pixel classification","author":"Loog","year":"2003","journal-title":"Medical Imaging 2003: Image Processing: International Society for Optics and Photonics"},{"key":"10.1016\/j.cmpb.2022.106627_bib0015","unstructured":"Hogeweg L.E. Automatic detection of tuberculosis in chest radiographs: [Sl: sn]; 2013."},{"key":"10.1016\/j.cmpb.2022.106627_bib0016","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1109\/TMI.2006.872747","article-title":"Segmentation of the posterior ribs in chest radiographs using iterated contextual pixel classification","volume":"25","author":"Loog","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2022.106627_bib0017","series-title":"5th International Conference on Biomedical Engineering","first-page":"194","article-title":"Ribs suppression in chest x-ray images by using ICA method","author":"Nguyen","year":"2015"},{"key":"10.1016\/j.cmpb.2022.106627_bib0018","unstructured":"Fonseca A.U., Vieira G.S., Soares F.A., Bulc\u00e3o-Neto R.F. Pediatric Chest Radiography Research Agenda: is Deep Learning Still in Childhood? arXiv preprint arXiv:200711369. 2020."},{"key":"10.1016\/j.cmpb.2022.106627_bib0019","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cmpb.2022.106627_bib0020","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"631","article-title":"Towards fully automatic X-ray to CT registration","author":"Esteban","year":"2019"},{"key":"10.1016\/j.cmpb.2022.106627_bib0021","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"444","article-title":"X-ray in-depth decomposition: revealing the latent structures","author":"Albarqouni","year":"2017"},{"key":"10.1016\/j.cmpb.2022.106627_bib0022","doi-asserted-by":"crossref","first-page":"3053","DOI":"10.1109\/TMI.2020.2986242","article-title":"High-resolution chest x-ray bone suppression using unpaired CT structural priors","volume":"39","author":"Li","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2022.106627_bib0023","first-page":"373","article-title":"World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects","volume":"79","year":"2001","journal-title":"Bull. World Health Organ."},{"key":"10.1016\/j.cmpb.2022.106627_bib0024","doi-asserted-by":"crossref","DOI":"10.3346\/jkms.2019.34.e250","article-title":"Added Value of Bone Suppression Image in the Detection of Subtle Lung Lesions on Chest Radiographs with Regard to Reader's Expertise","volume":"34","author":"Hong","year":"2019","journal-title":"J. Korean Med. Sci."},{"key":"10.1016\/j.cmpb.2022.106627_bib0025","doi-asserted-by":"crossref","first-page":"71","DOI":"10.2214\/ajr.174.1.1740071","article-title":"Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules","volume":"174","author":"Shiraishi","year":"2000","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.cmpb.2022.106627_bib0026","series-title":"Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention","first-page":"234","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.cmpb.2022.106627_bib0027","series-title":"European conference on computer vision","first-page":"694","article-title":"Perceptual losses for real-time style transfer and super-resolution","author":"Johnson","year":"2016"},{"key":"10.1016\/j.cmpb.2022.106627_bib0028","series-title":"European Conference on Computer Vision","first-page":"319","article-title":"Contrastive learning for unpaired image-to-image translation","author":"Park","year":"2020"},{"key":"10.1016\/j.cmpb.2022.106627_bib0029","series-title":"International conference on machine learning: PMLR;","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.cmpb.2022.106627_bib0030","unstructured":"Oord Avd, Li Y., Vinyals O. Representation learning with contrastive predictive coding. arXiv preprint arXiv:180703748. 2018."},{"key":"10.1016\/j.cmpb.2022.106627_bib0031","unstructured":"Kingma D.P., Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014."},{"key":"10.1016\/j.cmpb.2022.106627_bib0032","series-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"1125","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"key":"10.1016\/j.cmpb.2022.106627_bib0033","unstructured":"Yu F., Koltun V. Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:151107122. 2015."},{"key":"10.1016\/j.cmpb.2022.106627_bib0034","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: from error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.cmpb.2022.106627_bib0035","article-title":"Labeled optical coherence tomography (OCT) and Chest X-Ray images for classification","volume":"2","author":"Kermany","year":"2018","journal-title":"Mendeley data"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260722000128?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260722000128?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T11:23:45Z","timestamp":1673436225000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260722000128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3]]},"references-count":35,"alternative-id":["S0169260722000128"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2022.106627","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2022,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Bone suppression on pediatric chest radiographs via a deep learning-based cascade model","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2022.106627","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"106627"}}