{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,16]],"date-time":"2024-09-16T03:10:45Z","timestamp":1726456245564},"reference-count":24,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,12,1]],"date-time":"2018-12-01T00:00:00Z","timestamp":1543622400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.jp","clinicalkey.com","clinicalkey.es","clinicalkey.com.au","clinicalkey.fr","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1016\/j.compmedimag.2018.09.001","type":"journal-article","created":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T12:38:18Z","timestamp":1537187898000},"page":"1-7","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":32,"special_numbering":"C","title":["Semi-automatic lymphoma detection and segmentation using fully conditional random fields"],"prefix":"10.1016","volume":"70","author":[{"given":"Yuntao","family":"Yu","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5323-9910","authenticated-orcid":false,"given":"Pierre","family":"Decazes","sequence":"additional","affiliation":[]},{"given":"J\u00e9r\u00f4me","family":"Lapuyade-Lahorgue","sequence":"additional","affiliation":[]},{"given":"Isabelle","family":"Gardin","sequence":"additional","affiliation":[]},{"given":"Pierre","family":"Vera","sequence":"additional","affiliation":[]},{"given":"Su","family":"Ruan","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"Suppl. 1","key":"10.1016\/j.compmedimag.2018.09.001_bib0005","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s00259-017-3690-8","article-title":"FDG PET for therapy monitoring in Hodgkin and non-Hodgkin lymphomas","volume":"44","author":"Barrington","year":"2017","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"September","key":"10.1016\/j.compmedimag.2018.09.001_bib0010","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.compmedimag.2016.11.008","article-title":"Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies","volume":"60","author":"Bi","year":"2017","journal-title":"Comput. Med. Imaging Graph."},{"issue":"4","key":"10.1016\/j.compmedimag.2018.09.001_bib0015","doi-asserted-by":"crossref","first-page":"1272","DOI":"10.1016\/j.ijrobp.2004.06.254","article-title":"Defining a radiotherapy target with positron emission tomography","volume":"60","author":"Black","year":"2004","journal-title":"Int. J. Radiat. Oncol. Biol. Phys."},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0020","series-title":"Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D Images","author":"Boykov","year":"2011"},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0025","first-page":"118","article-title":"Simultaneous recovery of size and radioactivity concentration of small spheroids with PET data","volume":"40","author":"Chen","year":"1999","journal-title":"J. Nucl. Med."},{"issue":"15","key":"10.1016\/j.compmedimag.2018.09.001_bib0030","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.1158\/1078-0432.CCR-15-2825","article-title":"Molecular profile and FDG-PET\/CT total metabolic tumor volume improve risk classification at diagnosis for patients with diffuse large B-cell lymphoma","volume":"22","author":"Cottereau","year":"2016","journal-title":"Clin. Cancer Res. 1"},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0035","series-title":"3D Automated Lymphoma Segmentation in PET Images Based on Cellular Automata","author":"Desbordes","year":"2015"},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0040","series-title":"Symposium on Biomedical Imaging: From Nano to Macro","first-page":"174","article-title":"Automated 3D lymphoma lesion segmentation from PET\/CT characteristics","author":"Elo\u00efse","year":"2017"},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0045","first-page":"109","article-title":"Efficient inference in fully connected CRFs with gaussian edge potentials","volume":"24","author":"Kr\u00e4henb\u00fchl","year":"2011","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0050","series-title":"International Conference on Machine Learning (ICML)","article-title":"Parameter learning and convergent inference for dense random fields","author":"Kr\u00e4henb\u00fchl","year":"2013"},{"issue":"August 8","key":"10.1016\/j.compmedimag.2018.09.001_bib0055","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1080\/10428190903040048","article-title":"Report on the First International Workshop on interim-PET scan in lymphoma","volume":"50","author":"Meignan","year":"2009","journal-title":"Leuk. Lymphoma"},{"issue":"6","key":"10.1016\/j.compmedimag.2018.09.001_bib0060","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1007\/s00259-014-2705-y","article-title":"Metabolic tumour volumes measured at staging in lymphoma: methodological evaluation on phantom experiments and patients","volume":"41","author":"Meignan","year":"2014","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"},{"issue":"8","key":"10.1016\/j.compmedimag.2018.09.001_bib0065","first-page":"1342","article-title":"Comparison of different methods for delineation of 18F-FDG PET-positive tissue for target volume definition in radiotherapy of patients with non-Small cell lung cancer","volume":"46","author":"Nestle","year":"2005","journal-title":"J. Nucl. Med."},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0070","series-title":"Proceedings of 2010 IEEE International Symposium on on Circuits and Systems: Nano-Bio Circuit Fabrics and Systems (ISCAS '10)","first-page":"1783","article-title":"3D oncological PET volume analysis using CNN and LVQNN","author":"Sharif","year":"2010"},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0075","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s11263-007-0109-1","article-title":"TextonBoost for image understanding: multi-class object recognition and segmentation by jointly modeling texture, layout, and context","volume":"81","author":"Shotton","year":"2007","journal-title":"Int. J. Comput. Vis."},{"issue":"6","key":"10.1016\/j.compmedimag.2018.09.001_bib0080","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1109\/TPAMI.2007.70844","article-title":"A comparative study of energy minimization methods for Markov random fields with smoothness-based priors","volume":"30","author":"Szeliski","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0085","doi-asserted-by":"crossref","first-page":"268","DOI":"10.2967\/jnumed.109.066241","article-title":"Comparative assessment of methods for estimating tumor volume and standardized uptake value in (18) F-FDG PET","volume":"51","author":"Tylski","year":"2010","journal-title":"J. Nucl. Med."},{"issue":"February 2","key":"10.1016\/j.compmedimag.2018.09.001_bib0090","doi-asserted-by":"crossref","first-page":"268","DOI":"10.2967\/jnumed.109.066241","article-title":"Comparative assessment of methods for estimating tumor volume and standardized uptake value in F-18-FDG PET","volume":"51","author":"Tylski","year":"2010","journal-title":"J. Nucl. Med."},{"issue":"22","key":"10.1016\/j.compmedimag.2018.09.001_bib0095","doi-asserted-by":"crossref","first-page":"6901","DOI":"10.1088\/0031-9155\/54\/22\/010","article-title":"Development of a generic thresholding algorithm for the delineation of 18FDG-PET-positive tissue: application to the comparison of three thresholding models","volume":"54","author":"Vauclin","year":"2009","journal-title":"Phys. Med. Biol."},{"issue":"January 1","key":"10.1016\/j.compmedimag.2018.09.001_bib0100","first-page":"25","article-title":"(18) F-FDG avidity in lymphoma readdressed: a study of 766 patients","volume":"51","author":"Weiler-Sagie","year":"2009","journal-title":"J. Nucl. Med."},{"issue":"1","key":"10.1016\/j.compmedimag.2018.09.001_bib0105","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2015.05.009","article-title":"Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning","volume":"24","author":"Zu","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0110","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/978-3-662-45657-6_21","article-title":"Classification of lymphoma cell image based on improved SVM","volume":"332","author":"Yan","year":"2015","journal-title":"Lecture Notes Electr. Eng."},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0115","doi-asserted-by":"crossref","unstructured":"Yu Y., Decazes P., Gardin I., Vera P., Ruan S. 3D Lymphoma Segmentation in PET\/CT Images Based on Fully Connected CRFs. Cardoso M. et al. Molecular Imaging, Reconstruction and Analysis of Moving Body Organs, and Stroke Imaging and Treatment. CMMI 2017, SWITCH 2017, RAMBO 2017. Lecture Notes in Computer Science, vol. 10555. Springer, Cham.","DOI":"10.1007\/978-3-319-67564-0_1"},{"key":"10.1016\/j.compmedimag.2018.09.001_bib0120","doi-asserted-by":"crossref","first-page":"2165","DOI":"10.1007\/s00259-010-1423-3","article-title":"PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques","volume":"37","author":"Zaidi","year":"2010","journal-title":"Eur. J. Nucl. Med. Mol. Imaging"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611118301307?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611118301307?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,10,24]],"date-time":"2019-10-24T05:12:00Z","timestamp":1571893920000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611118301307"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12]]},"references-count":24,"alternative-id":["S0895611118301307"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2018.09.001","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2018,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Semi-automatic lymphoma detection and segmentation using fully conditional random fields","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2018.09.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}