{"id":"https://openalex.org/W4308237652","doi":"https://doi.org/10.1109/icip46576.2022.9898057","title":"Residual U-Structure Nested Conditional Adversarial Nets Colorized CT Improves Deep Learning Based Abdominal Multi-Organ Segmentation","display_name":"Residual U-Structure Nested Conditional Adversarial Nets Colorized CT Improves Deep Learning Based Abdominal Multi-Organ Segmentation","publication_year":2022,"publication_date":"2022-10-16","ids":{"openalex":"https://openalex.org/W4308237652","doi":"https://doi.org/10.1109/icip46576.2022.9898057"},"language":"en","primary_location":{"id":"doi:10.1109/icip46576.2022.9898057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898057","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","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/A5085233971","display_name":"Vincent Chandra","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Vincent Chandra","raw_affiliation_strings":["Xiamen University,Department of Computer Science,Xiamen,China,361005"],"affiliations":[{"raw_affiliation_string":"Xiamen University,Department of Computer Science,Xiamen,China,361005","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044224149","display_name":"Wenkang Fan","orcid":"https://orcid.org/0000-0002-8364-5159"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenkang Fan","raw_affiliation_strings":["Xiamen University,Department of Computer Science,Xiamen,China,361005"],"affiliations":[{"raw_affiliation_string":"Xiamen University,Department of Computer Science,Xiamen,China,361005","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082244615","display_name":"Yinran Chen","orcid":"https://orcid.org/0000-0001-8062-1859"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinran Chen","raw_affiliation_strings":["Xiamen University,Department of Computer Science,Xiamen,China,361005"],"affiliations":[{"raw_affiliation_string":"Xiamen University,Department of Computer Science,Xiamen,China,361005","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100426934","display_name":"Xi\u00f3ngbi\u0101o Lu\u00f3","orcid":"https://orcid.org/0000-0002-5238-489X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongbiao Luo","raw_affiliation_strings":["Xiamen University,Department of Computer Science,Xiamen,China,361005"],"affiliations":[{"raw_affiliation_string":"Xiamen University,Department of Computer Science,Xiamen,China,361005","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5085233971"],"corresponding_institution_ids":["https://openalex.org/I191208505"],"apc_list":null,"apc_paid":null,"fwci":0.2234,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4584643,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"2061","last_page":"2065"},"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":0.9988999962806702,"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":0.9988999962806702,"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":0.9984999895095825,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9945999979972839,"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":0.7291138768196106},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6925306916236877},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5999127626419067},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.5869529247283936},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.56144118309021},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.5562117099761963},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.548607587814331},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4738108217716217},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40483370423316956},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1938532590866089}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7291138768196106},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6925306916236877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5999127626419067},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.5869529247283936},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.56144118309021},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5562117099761963},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.548607587814331},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4738108217716217},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40483370423316956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1938532590866089}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip46576.2022.9898057","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip46576.2022.9898057","pdf_url":null,"source":{"id":"https://openalex.org/S4363607719","display_name":"2022 IEEE International Conference on Image Processing (ICIP)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2464708700","https://openalex.org/W2580772840","https://openalex.org/W2592929672","https://openalex.org/W2708692267","https://openalex.org/W2791680898","https://openalex.org/W2945263066","https://openalex.org/W2962914239","https://openalex.org/W2963073614","https://openalex.org/W3008774220","https://openalex.org/W3025800305","https://openalex.org/W3118839427","https://openalex.org/W3122239309"],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2154063878","https://openalex.org/W2904870937","https://openalex.org/W3209312100","https://openalex.org/W3197673523","https://openalex.org/W3047746737","https://openalex.org/W4287691568","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,123],"abdominal":[2,20,91,130],"organs":[3],"such":[4],"as":[5],"the":[6,104,110,121],"liver,":[7],"pancreas,":[8],"spleen,":[9],"and":[10,18,39,69,84,87,108,125],"kidneys":[11],"plays":[12],"an":[13],"essential":[14],"role":[15],"in":[16],"diagnosing":[17],"treating":[19],"diseases.":[21],"Although":[22],"numerous":[23],"deeply":[24],"learned":[25],"segmentation":[26,54],"methods":[27],"work":[28],"well,":[29],"they":[30],"still":[31],"suffer":[32],"from":[33,113],"partial":[34],"volume":[35],"effects,":[36],"image":[37,74],"noise,":[38],"data":[40],"imbalance.":[41],"This":[42],"study":[43],"aims":[44],"to":[45,49,82,116],"colorize":[46],"CT":[47,73,78,100],"images":[48,79,101],"boost":[50],"or":[51],"augment":[52],"these":[53],"approaches.":[55],"We":[56],"propose":[57],"new":[58],"residual":[59,67],"U-structure":[60],"nested":[61],"generative":[62],"adversarial":[63],"nets":[64],"that":[65,98],"use":[66],"U-blocks":[68],"spectral":[70],"normalization":[71],"for":[72,89,128],"colorization.":[75],"Generated":[76],"color":[77],"were":[80],"introduced":[81],"train":[83],"validate":[85],"V-Net":[86,124,127],"DenseV-Net":[88],"multiple":[90,129],"organ":[92,131],"segmentation.":[93,132],"The":[94],"experimental":[95],"results":[96],"demonstrate":[97],"colorized":[99],"can":[102],"improve":[103],"dice":[105],"similarity":[106],"coefficient":[107],"reduce":[109],"Hausdorff":[111],"distance":[112],"(0.32,":[114],"302.7)":[115],"(0.67,":[117],"78.2),":[118],"significantly":[119],"boosting":[120],"performance":[122],"Dense":[126]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}