{"id":"https://openalex.org/W3133333029","doi":"https://doi.org/10.1117/12.2581126","title":"Residual mask scoring regional convolutional neural network for multi-organ segmentation in head-and-neck CT","display_name":"Residual mask scoring regional convolutional neural network for multi-organ segmentation in head-and-neck CT","publication_year":2021,"publication_date":"2021-02-12","ids":{"openalex":"https://openalex.org/W3133333029","doi":"https://doi.org/10.1117/12.2581126","mag":"3133333029"},"language":"en","primary_location":{"id":"doi:10.1117/12.2581126","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2581126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging","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/A5026661364","display_name":"Xianjin Dai","orcid":"https://orcid.org/0000-0002-4629-1996"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xianjin Dai","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011903902","display_name":"Yang Lei","orcid":"https://orcid.org/0000-0002-3572-0345"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yang Lei","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049656223","display_name":"Tonghe Wang","orcid":"https://orcid.org/0000-0001-9021-1204"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tonghe Wang","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043052005","display_name":"Jun Zhou","orcid":"https://orcid.org/0000-0002-6078-9424"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jun Zhou","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108367355","display_name":"Justin Roper","orcid":null},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin Roper","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086616055","display_name":"Mark W. McDonald","orcid":"https://orcid.org/0000-0003-3021-9356"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark McDonald","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053851809","display_name":"Jonathan J. Beitler","orcid":"https://orcid.org/0000-0002-1519-2071"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Beitler","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026088869","display_name":"Walter J. Curran","orcid":"https://orcid.org/0000-0002-7552-4453"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Walter J. Curran","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365649","display_name":"Tian Liu","orcid":"https://orcid.org/0000-0002-2906-5110"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tian Liu","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100619090","display_name":"Xiaofeng Yang","orcid":"https://orcid.org/0000-0001-9023-5855"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaofeng Yang","raw_affiliation_strings":["The Winship Cancer Institute of Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Winship Cancer Institute of Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150468666"],"apc_list":null,"apc_paid":null,"fwci":3.9322,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.91157441,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"56","last_page":"56"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10358","display_name":"Advanced Radiotherapy Techniques","score":0.9987999796867371,"subfield":{"id":"https://openalex.org/subfields/3108","display_name":"Radiation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical 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"}},{"id":"https://openalex.org/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/contouring","display_name":"Contouring","score":0.8746457099914551},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6739174127578735},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.644226610660553},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.6296596527099609},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6135950088500977},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6059088110923767},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5563576817512512},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5474260449409485},{"id":"https://openalex.org/keywords/head-and-neck","display_name":"Head and neck","score":0.5049343705177307},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.47648805379867554},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.4547443389892578},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.4424605071544647},{"id":"https://openalex.org/keywords/percentile","display_name":"Percentile","score":0.41385719180107117},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3163558840751648},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23154836893081665},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.22067946195602417},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.10506078600883484}],"concepts":[{"id":"https://openalex.org/C2779104521","wikidata":"https://www.wikidata.org/wiki/Q23058469","display_name":"Contouring","level":2,"score":0.8746457099914551},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6739174127578735},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.644226610660553},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.6296596527099609},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6135950088500977},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6059088110923767},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5563576817512512},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5474260449409485},{"id":"https://openalex.org/C3018411727","wikidata":"https://www.wikidata.org/wiki/Q3867488","display_name":"Head and neck","level":2,"score":0.5049343705177307},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.47648805379867554},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.4547443389892578},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.4424605071544647},{"id":"https://openalex.org/C122048520","wikidata":"https://www.wikidata.org/wiki/Q2913954","display_name":"Percentile","level":2,"score":0.41385719180107117},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3163558840751648},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23154836893081665},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.22067946195602417},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.10506078600883484},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C141071460","wikidata":"https://www.wikidata.org/wiki/Q40821","display_name":"Surgery","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2581126","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2581126","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7099999785423279,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2994739006","https://openalex.org/W2094359191","https://openalex.org/W2972534068","https://openalex.org/W3089117938","https://openalex.org/W3127600691","https://openalex.org/W3188463548","https://openalex.org/W4310691713","https://openalex.org/W1121315442","https://openalex.org/W4363650189","https://openalex.org/W4387870815"],"abstract_inverted_index":{"The":[0,15,125,251],"delineation":[1,76,149],"of":[2,17,27,32,147,159,200,211,243],"target":[3,19],"and":[4,20,45,89,122,131,163,188,209,230],"organs-at-risk":[5],"(OARs)":[6],"is":[7,34,42,150,254],"a":[8,71,104],"necessary":[9],"step":[10],"in":[11,93,129,134,138,171,256],"radiotherapy":[12,28,260],"treatment":[13,261],"planning.":[14,262],"accuracy":[16,146],"the":[18,25,35,97,145,157,198,201,244],"OAR":[21,91,148],"contours":[22,92],"directly":[23],"affects":[24],"quality":[26],"plans.":[29],"Manual":[30],"contouring":[31],"OARs":[33,57,246],"routine":[36],"procedure":[37],"at":[38],"present,":[39],"which,":[40],"however,":[41],"very":[43],"time-consuming":[44],"requires":[46],"significant":[47],"expertise,":[48],"especially":[49],"for":[50,259],"those":[51,142],"head-and-neck":[52],"(HN)":[53],"cancer":[54],"cases,":[55],"where":[56],"densely":[58],"distribute":[59],"around":[60],"tumors":[61],"with":[62],"complex":[63],"anatomical":[64],"structures.":[65],"In":[66,96],"this":[67],"study,":[68],"we":[69],"propose":[70],"deep":[72],"learning-based":[73],"fully":[74],"automated":[75],"method,":[77],"namely,":[78],"mask":[79],"scoring":[80],"regional":[81],"convolutional":[82],"neural":[83],"network":[84,108,114],"(MS-RCNN),":[85],"to":[86,117,152,156,196],"obtain":[87],"consistent":[88],"reliable":[90],"HN":[94],"CT.":[95],"model,":[98],"MR":[99],"images":[100],"were":[101,194,238],"synthesized":[102],"by":[103,247],"cycle-consistent":[105],"generative":[106],"adversarial":[107],"given":[109],"CT":[110,123,130],"images.":[111],"A":[112],"backbone":[113,170],"was":[115,167],"utilized":[116],"extract":[118],"features":[119],"from":[120],"MRI":[121,135],"independently.":[124],"high":[126],"bony-structure":[127],"contrast":[128,133],"soft-tissue":[132],"are":[136],"complementary":[137,143],"nature.":[139],"Through":[140],"combining":[141],"contrasts,":[144],"expected":[151],"be":[153],"improved.":[154],"Due":[155],"ability":[158],"various":[160],"object":[161],"detection":[162],"classification,":[164],"ResNet":[165],"101":[166],"used":[168],"as":[169],"MS-RCNN.":[172],"Quantities":[173],"including":[174],"Dice":[175],"similarity":[176],"coefficient":[177],"(DSC),":[178],"95th":[179],"percentile":[180],"Hausdorff":[181],"distance":[182,186,192],"(HD95),":[183],"mean":[184,190],"surface":[185],"(MSD),":[187],"residual":[189],"square":[191],"(RMS)":[193],"calculated":[195],"evaluate":[197],"performance":[199],"proposed":[202,249,252],"method.":[203,250],"An":[204],"average":[205],"DSC,":[206],"HD95,":[207],"MSD":[208],"RMS":[210],"0.78":[212],"(0.58":[213],"-":[214,220,227,235],"0.89),":[215],"4.88":[216],"mm":[217,219,224,226,232,234],"(2.79":[218],"7.46":[221],"mm),":[222,229,237],"1.39":[223],"(0.69":[225],"1.99":[228],"2.23":[231],"(1.30":[233],"3.23":[236],"respectively":[239],"achieved":[240],"across":[241],"all":[242],"12":[245],"our":[248],"method":[253],"promising":[255],"facilitating":[257],"auto-contouring":[258]},"counts_by_year":[{"year":2022,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
