{"id":"https://openalex.org/W3011707746","doi":"https://doi.org/10.1117/12.2548806","title":"Automatic lung segmentation in low-dose CT image with contrastive attention module","display_name":"Automatic lung segmentation in low-dose CT image with contrastive attention module","publication_year":2020,"publication_date":"2020-03-10","ids":{"openalex":"https://openalex.org/W3011707746","doi":"https://doi.org/10.1117/12.2548806","mag":"3011707746"},"language":"en","primary_location":{"id":"doi:10.1117/12.2548806","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2548806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","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/A5043270984","display_name":"Yang Changxing","orcid":null},"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":true,"raw_author_name":"Changxing Yang","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045518534","display_name":"Haihong Tian","orcid":"https://orcid.org/0000-0002-7052-9239"},"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":"Haihong Tian","raw_affiliation_strings":["Soochow Univ. (China)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","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)"],"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)"],"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)"],"affiliations":[{"raw_affiliation_string":"Soochow Univ. (China)","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","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)"],"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":["https://openalex.org/A5043270984"],"corresponding_institution_ids":["https://openalex.org/I3923682"],"apc_list":null,"apc_paid":null,"fwci":0.1101,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.46882256,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"110","last_page":"110"},"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":0.913100004196167,"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":0.913100004196167,"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/computer-science","display_name":"Computer science","score":0.6555371284484863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.642505407333374},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5905020236968994},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5644368529319763},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4569630026817322}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6555371284484863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.642505407333374},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5905020236968994},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5644368529319763},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4569630026817322}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2548806","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2548806","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2020: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398","https://openalex.org/W2775347418"],"abstract_inverted_index":{"Automatic":[0],"lung":[1,21,25,73,112,139],"segmentation":[2,83,177],"with":[3],"severe":[4],"pathology":[5],"plays":[6],"a":[7,52,69,78,93,99,117,154],"significant":[8],"role":[9],"in":[10,29,86],"the":[11,24,36,43,48,131,134,138,144,163],"clinical":[12,155],"application,":[13],"which":[14,97],"can":[15],"save":[16],"physicians\u2019":[17],"efforts":[18],"to":[19,55,60,107,129],"annotate":[20],"anatomy.":[22],"Since":[23],"has":[26],"fuzzy":[27],"boundary":[28],"low-dose":[30,87],"computed":[31],"tomography":[32],"(CT)":[33],"images,":[34],"and":[35,38,64,103,113,137],"tracheas":[37],"other":[39],"tissues":[40],"generally":[41],"have":[42],"similar":[44],"gray":[45],"value":[46],"as":[47],"lung,":[49],"it":[50],"is":[51,68,120],"challenging":[53],"task":[54],"accurately":[56],"segment":[57],"lung.":[58],"How":[59],"extract":[61],"key":[62],"features":[63,67,132,145],"remove":[65],"background":[66,104,114,147],"core":[70],"problem":[71],"for":[72,81],"segmentation.":[74],"This":[75],"paper":[76],"introduces":[77],"novel":[79],"approach":[80],"automatic":[82],"of":[84,101,111,158,181],"lungs":[85],"CT":[88,160],"images.":[89],"First,":[90],"we":[91],"propose":[92],"contrastive":[94],"attention":[95,105],"module,":[96],"generates":[98],"pair":[100],"foreground":[102],"maps":[106],"guide":[108],"feature":[109,124],"learning":[110],"separately.":[115],"Second,":[116],"triplet":[118],"loss":[119],"used":[121],"on":[122,153],"three":[123],"vectors":[125],"from":[126,133,146],"different":[127],"regions":[128],"pull":[130],"full":[135],"image":[136],"region":[140],"close":[141],"whereas":[142],"pushing":[143],"away.":[148],"Our":[149],"method":[150,173],"was":[151],"validated":[152],"data":[156],"set":[157],"78":[159],"scans":[161],"using":[162],"four-fold":[164],"cross":[165],"validation":[166],"strategy.":[167],"Experimental":[168],"results":[169,178],"showed":[170],"that":[171,180],"our":[172],"achieved":[174],"more":[175],"accurate":[176],"than":[179],"state-of-the-art":[182],"approaches.":[183]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
