{"id":"https://openalex.org/W2440714592","doi":"https://doi.org/10.1109/isbi.2016.7493421","title":"Learning multi-modality local and global affinities in graph based ranking for automated lung tumor delineation","display_name":"Learning multi-modality local and global affinities in graph based ranking for automated lung tumor delineation","publication_year":2016,"publication_date":"2016-04-01","ids":{"openalex":"https://openalex.org/W2440714592","doi":"https://doi.org/10.1109/isbi.2016.7493421","mag":"2440714592"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2016.7493421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2016.7493421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5004971220","display_name":"Hui Cui","orcid":"https://orcid.org/0000-0001-8224-4698"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Hui Cui","raw_affiliation_strings":["BMIT research group, University of Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BMIT research group, University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046614213","display_name":"Xiuying Wang","orcid":"https://orcid.org/0000-0001-7160-5929"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Xiuying Wang","raw_affiliation_strings":["BMIT research group, University of Sydney, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BMIT research group, University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047165067","display_name":"Jianlong Zhou","orcid":"https://orcid.org/0000-0001-6034-644X"},"institutions":[{"id":"https://openalex.org/I42894916","display_name":"Data61","ror":"https://ror.org/03q397159","country_code":"AU","type":"other","lineage":["https://openalex.org/I1292875679","https://openalex.org/I2801453606","https://openalex.org/I42894916","https://openalex.org/I4387156119"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Jianlong Zhou","raw_affiliation_strings":["National ICT, Australia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"National ICT, Australia","institution_ids":["https://openalex.org/I42894916"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070206463","display_name":"Guanzhong Gong","orcid":"https://orcid.org/0000-0002-4943-8084"},"institutions":[{"id":"https://openalex.org/I4210100830","display_name":"Shandong Tumor Hospital","ror":"https://ror.org/01413r497","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210100830"]},{"id":"https://openalex.org/I4210163399","display_name":"Shandong First Medical University","ror":"https://ror.org/05jb9pq57","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163399"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanzhong Gong","raw_affiliation_strings":["Shandong Tumor Hospital, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Tumor Hospital, China","institution_ids":["https://openalex.org/I4210100830","https://openalex.org/I4210163399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100715072","display_name":"Yong Yin","orcid":"https://orcid.org/0000-0003-1090-4313"},"institutions":[{"id":"https://openalex.org/I4210100830","display_name":"Shandong Tumor Hospital","ror":"https://ror.org/01413r497","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210100830"]},{"id":"https://openalex.org/I4210163399","display_name":"Shandong First Medical University","ror":"https://ror.org/05jb9pq57","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163399"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yong Yin","raw_affiliation_strings":["Shandong Tumor Hospital, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Tumor Hospital, China","institution_ids":["https://openalex.org/I4210100830","https://openalex.org/I4210163399"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101552747","display_name":"Zheng Fu","orcid":"https://orcid.org/0000-0002-7483-832X"},"institutions":[{"id":"https://openalex.org/I4210100830","display_name":"Shandong Tumor Hospital","ror":"https://ror.org/01413r497","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210100830"]},{"id":"https://openalex.org/I4210163399","display_name":"Shandong First Medical University","ror":"https://ror.org/05jb9pq57","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210163399"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fu Zheng","raw_affiliation_strings":["Shandong Tumor Hospital, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shandong Tumor Hospital, China","institution_ids":["https://openalex.org/I4210100830","https://openalex.org/I4210163399"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068891693","display_name":"Dagan Feng","orcid":"https://orcid.org/0000-0002-3381-214X"},"institutions":[{"id":"https://openalex.org/I129604602","display_name":"The University of Sydney","ror":"https://ror.org/0384j8v12","country_code":"AU","type":"education","lineage":["https://openalex.org/I129604602"]},{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["AU","CN"],"is_corresponding":false,"raw_author_name":"Dagan Feng","raw_affiliation_strings":["BMIT research group, University of Sydney, Australia","Med-X Research Institute, Shanghai Jiao Tong University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BMIT research group, University of Sydney, Australia","institution_ids":["https://openalex.org/I129604602"]},{"raw_affiliation_string":"Med-X Research Institute, Shanghai Jiao Tong University, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":null,"first_page":"948","last_page":"951"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6536716222763062},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6191204786300659},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4775705933570862},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.46697449684143066},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4585556387901306},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4557998776435852},{"id":"https://openalex.org/keywords/pet-ct","display_name":"PET-CT","score":0.43683552742004395},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.418867290019989},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.4171627461910248},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.38274261355400085},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.3647472858428955},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.25263047218322754},{"id":"https://openalex.org/keywords/positron-emission-tomography","display_name":"Positron emission tomography","score":0.23432743549346924},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.0850544273853302}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6536716222763062},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6191204786300659},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4775705933570862},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.46697449684143066},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4585556387901306},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4557998776435852},{"id":"https://openalex.org/C127077266","wikidata":"https://www.wikidata.org/wiki/Q3488638","display_name":"PET-CT","level":3,"score":0.43683552742004395},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.418867290019989},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.4171627461910248},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.38274261355400085},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.3647472858428955},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.25263047218322754},{"id":"https://openalex.org/C2775842073","wikidata":"https://www.wikidata.org/wiki/Q208376","display_name":"Positron emission tomography","level":2,"score":0.23432743549346924},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0850544273853302}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2016.7493421","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2016.7493421","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5299999713897705,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W96430563","https://openalex.org/W748803803","https://openalex.org/W1967362871","https://openalex.org/W1971864936","https://openalex.org/W1980645488","https://openalex.org/W1989368986","https://openalex.org/W2080593651","https://openalex.org/W2104290444","https://openalex.org/W2111744777","https://openalex.org/W2125637308","https://openalex.org/W2129766480","https://openalex.org/W2143516773","https://openalex.org/W2154455818","https://openalex.org/W2165820735","https://openalex.org/W2170552969","https://openalex.org/W2798247327","https://openalex.org/W2997701990","https://openalex.org/W6603948899","https://openalex.org/W6622207100","https://openalex.org/W6675747103","https://openalex.org/W6682494755","https://openalex.org/W6750455528"],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W2374013449","https://openalex.org/W73545470","https://openalex.org/W2188500270","https://openalex.org/W2303858293","https://openalex.org/W2364381299","https://openalex.org/W2915512527","https://openalex.org/W2374430585","https://openalex.org/W1522196789"],"abstract_inverted_index":{"With":[0],"the":[1,48,63,92,111,115,122,152],"rapid":[2],"growth":[3],"of":[4,10,17,67,125],"biomedical":[5],"imaging":[6],"data,":[7],"manual":[8,117],"delineation":[9,118],"gross":[11],"tumor":[12,49],"volume":[13],"(GTV)":[14],"for":[15,69,80,94],"variety":[16],"cancers":[18],"is":[19],"becoming":[20],"less":[21],"practical":[22],"due":[23],"to":[24,46,90,151],"its":[25],"low":[26],"efficiency,":[27],"non-reproducibility,":[28],"and":[29,50,55,74,120,131],"inter-observer":[30],"dependency.":[31],"In":[32],"this":[33],"paper,":[34],"we":[35],"propose":[36],"an":[37,70],"automated":[38],"co-segmentation":[39],"method":[40,113],"using":[41],"a":[42,140],"Bayesian":[43],"decision":[44],"theory":[45],"correlate":[47],"background":[51],"similarities":[52,93],"from":[53,78],"PET":[54,79],"CT":[56,68],"images.":[57],"Our":[58],"algorithm":[59,89],"takes":[60],"into":[61],"account":[62],"local":[64],"intensity":[65],"variations":[66],"accurate":[71],"boundary":[72],"definition":[73],"global":[75],"topology":[76],"extracted":[77],"compensating":[81],"heterogeneous":[82],"FDG":[83],"uptake.":[84],"We":[85],"use":[86],"manifold":[87],"ranking":[88],"estimate":[91],"classification.":[95],"The":[96,106,137],"segmentation":[97,147],"was":[98],"evaluated":[99],"on":[100],"20":[101],"PET/CT":[102],"NSCLC":[103],"patient":[104],"studies.":[105],"experimental":[107],"results":[108],"demonstrated":[109,139],"that":[110],"proposed":[112],"decreased":[114],"inter-modality":[116],"variance":[119],"achieved":[121],"average":[123],"DSC":[124],"0.834\u00b10.054":[126],"when":[127,133,149],"compared":[128,134,150],"with":[129,135],"GTVpet":[130],"0.815\u00b10.075":[132],"GTVct.":[136],"t-test":[138],"statistically":[141],"significant":[142],"improvement":[143],"(p":[144],"<.001)":[145],"in":[146],"accuracy":[148],"other":[153],"three":[154],"graph":[155],"based":[156],"methods.":[157]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
