{"id":"https://openalex.org/W2317109106","doi":"https://doi.org/10.1117/12.2216229","title":"A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models","display_name":"A novel 3D graph cut based co-segmentation of lung tumor on PET-CT images with Gaussian mixture models","publication_year":2016,"publication_date":"2016-03-21","ids":{"openalex":"https://openalex.org/W2317109106","doi":"https://doi.org/10.1117/12.2216229","mag":"2317109106"},"language":"en","primary_location":{"id":"doi:10.1117/12.2216229","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2216229","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},"type":"article","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/A5100692616","display_name":"Kai Yu","orcid":"https://orcid.org/0000-0001-6752-6216"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Yu","raw_affiliation_strings":["Soochow Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079807652","display_name":"Xinjian Chen","orcid":"https://orcid.org/0000-0002-0871-293X"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjian Chen","raw_affiliation_strings":["Soochow Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048091475","display_name":"Fei Shi","orcid":"https://orcid.org/0000-0002-8878-6655"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Shi","raw_affiliation_strings":["Soochow Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058024483","display_name":"Weifang Zhu","orcid":"https://orcid.org/0000-0001-9540-4101"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weifang Zhu","raw_affiliation_strings":["Soochow Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100392769","display_name":"Bin Zhang","orcid":"https://orcid.org/0000-0001-5305-2494"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]},{"id":"https://openalex.org/I4210153519","display_name":"First Affiliated Hospital of Soochow University","ror":"https://ror.org/051jg5p78","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210153519"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bin Zhang","raw_affiliation_strings":["The First Affiliated Hospital of Soochow Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The First Affiliated Hospital of Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682","https://openalex.org/I4210153519"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031688232","display_name":"Dehui Xiang","orcid":"https://orcid.org/0000-0001-7873-9778"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dehui Xiang","raw_affiliation_strings":["Soochow Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.5071,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.71202422,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":"9784","issue":null,"first_page":"97842V","last_page":"97842V"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9995999932289124,"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"}},"topics":[{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9995999932289124,"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"}},{"id":"https://openalex.org/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9973000288009644,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9965999722480774,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5548351407051086},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5540850162506104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5443143844604492},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5184809565544128},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.5162684917449951},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5042260885238647},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4923473596572876},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.4769274592399597},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3931570053100586},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1546434760093689},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11690840125083923}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5548351407051086},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5540850162506104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5443143844604492},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5184809565544128},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.5162684917449951},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5042260885238647},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4923473596572876},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.4769274592399597},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3931570053100586},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1546434760093689},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11690840125083923},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2216229","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2216229","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5899999737739563,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1910102824","https://openalex.org/W2119300483","https://openalex.org/W2125637308","https://openalex.org/W6640105227","https://openalex.org/W6678933642"],"related_works":["https://openalex.org/W1578916557","https://openalex.org/W1522196789","https://openalex.org/W2021544484","https://openalex.org/W2032319136","https://openalex.org/W2109407305","https://openalex.org/W2038231398","https://openalex.org/W1582388844","https://openalex.org/W2088651901","https://openalex.org/W2897997384","https://openalex.org/W1544828638"],"abstract_inverted_index":{"Positron":[0],"Emission":[1],"Tomography":[2,6],"(PET)":[3],"and":[4,92,102,138,145],"Computed":[5],"(CT)":[7],"have":[8],"been":[9],"widely":[10],"used":[11,22],"in":[12,37,42],"clinical":[13],"practice":[14],"for":[15,81],"radiation":[16],"therapy.":[17],"Most":[18],"existing":[19],"methods":[20],"only":[21],"one":[23],"image":[24],"modality,":[25],"either":[26],"PET":[27,38,91],"or":[28,39],"CT,":[29],"which":[30,55,109],"suffers":[31],"from":[32],"the":[33,62,69,82,85,107,111,115,119,125,139,143,148],"low":[34,40],"spatial":[35],"resolution":[36],"contrast":[41],"CT.":[43],"In":[44],"this":[45],"paper,":[46],"a":[47,103],"novel":[48],"3D":[49],"graph":[50,63,86,97],"cut":[51,64,87],"method":[52,72,130],"is":[53,122],"proposed,":[54],"integrated":[56],"Gaussian":[57],"Mixture":[58],"Models":[59],"(GMMs)":[60],"into":[61],"method.":[65,127],"We":[66],"also":[67],"employed":[68],"random":[70],"walk":[71],"as":[73],"an":[74],"initialization":[75],"step":[76],"to":[77],"provide":[78],"object":[79],"seeds":[80],"improvement":[83],"of":[84,99,147],"based":[88],"segmentation":[89,113,120],"on":[90,133],"CT":[93],"images.":[94],"The":[95,128],"constructed":[96],"consists":[98],"two":[100,116],"sub-graphs":[101,108],"special":[104],"link":[105],"between":[106,114],"penalize":[110],"difference":[112],"modalities.":[117],"Finally,":[118],"problem":[121],"solved":[123],"by":[124],"max-flow/min-cut":[126],"proposed":[129,149],"was":[131],"tested":[132],"20":[134],"patients\u2019":[135],"PET-CT":[136],"images,":[137],"experimental":[140],"results":[141],"demonstrated":[142],"accuracy":[144],"efficiency":[146],"algorithm.":[150]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2018,"cited_by_count":3}],"updated_date":"2026-06-16T09:24:06.705377","created_date":"2025-10-10T00:00:00"}
