{"id":"https://openalex.org/W2972339784","doi":"https://doi.org/10.1109/lsp.2019.2940926","title":"Edge-Guided Output Adaptor: Highly Efficient Adaptation Module for Cross-Vendor Medical Image Segmentation","display_name":"Edge-Guided Output Adaptor: Highly Efficient Adaptation Module for Cross-Vendor Medical Image Segmentation","publication_year":2019,"publication_date":"2019-09-11","ids":{"openalex":"https://openalex.org/W2972339784","doi":"https://doi.org/10.1109/lsp.2019.2940926","mag":"2972339784"},"language":"en","primary_location":{"id":"doi:10.1109/lsp.2019.2940926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2940926","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-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/A5100652053","display_name":"Wenjun Yan","orcid":"https://orcid.org/0000-0002-6055-8677"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wenjun Yan","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423182","display_name":"Yuanyuan Wang","orcid":"https://orcid.org/0000-0003-1984-1136"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Wang","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011169234","display_name":"Menghua Xia","orcid":"https://orcid.org/0000-0002-9503-1381"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Menghua Xia","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028217553","display_name":"Qian Tao","orcid":"https://orcid.org/0000-0001-7480-0703"},"institutions":[{"id":"https://openalex.org/I2800006345","display_name":"Leiden University Medical Center","ror":"https://ror.org/05xvt9f17","country_code":"NL","type":"funder","lineage":["https://openalex.org/I2800006345"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Qian Tao","raw_affiliation_strings":["Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands"],"affiliations":[{"raw_affiliation_string":"Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands","institution_ids":["https://openalex.org/I2800006345"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100652053"],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":1.9422,"has_fulltext":false,"cited_by_count":42,"citation_normalized_percentile":{"value":0.89388135,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"26","issue":"11","first_page":"1593","last_page":"1597"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9976999759674072,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9976999759674072,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9959999918937683,"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"}},{"id":"https://openalex.org/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9958999752998352,"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":0.7855921983718872},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7619326114654541},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6282379031181335},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.575186550617218},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5550534725189209},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4919719398021698},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4878104031085968},{"id":"https://openalex.org/keywords/canny-edge-detector","display_name":"Canny edge detector","score":0.45824238657951355},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.3315203785896301},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.24974089860916138},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.22039735317230225}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7855921983718872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7619326114654541},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6282379031181335},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.575186550617218},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5550534725189209},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4919719398021698},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4878104031085968},{"id":"https://openalex.org/C14705441","wikidata":"https://www.wikidata.org/wiki/Q597183","display_name":"Canny edge detector","level":5,"score":0.45824238657951355},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.3315203785896301},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.24974089860916138},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.22039735317230225}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/lsp.2019.2940926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lsp.2019.2940926","pdf_url":null,"source":{"id":"https://openalex.org/S120629676","display_name":"IEEE Signal Processing Letters","issn_l":"1070-9908","issn":["1070-9908","1558-2361"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"journal-article"},{"id":"pmh:lumc:oai:openaccess.leidenuniv.nl:1887/121836","is_oa":false,"landing_page_url":"http://hdl.handle.net/1887/121836","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"VOLUME=26;ISSUE=11;STARTPAGE=1593;ENDPAGE=1597;TITLE=None","raw_type":"info:eu-repo/semantics/article"},{"id":"pmh:oai:scholarlypublications.universiteitleiden.nl:item_2987134","is_oa":false,"landing_page_url":"https://hdl.handle.net/1887/121836","pdf_url":null,"source":{"id":"https://openalex.org/S4306400850","display_name":"Leiden Repository (Leiden University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I121797337","host_organization_name":"Leiden University","host_organization_lineage":["https://openalex.org/I121797337"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Signal Processing Letters","raw_type":"Article / Letter to editor"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"awards":[{"id":"https://openalex.org/G1861648665","display_name":null,"funder_award_id":"2018YFC0116303","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1731081199","https://openalex.org/W1901129140","https://openalex.org/W2099471712","https://openalex.org/W2145023731","https://openalex.org/W2412782625","https://openalex.org/W2521803624","https://openalex.org/W2584009249","https://openalex.org/W2592929672","https://openalex.org/W2593768305","https://openalex.org/W2594179300","https://openalex.org/W2613041730","https://openalex.org/W2623776388","https://openalex.org/W2762672048","https://openalex.org/W2803176574","https://openalex.org/W2805899143","https://openalex.org/W2895281799","https://openalex.org/W2897944447","https://openalex.org/W2910742108","https://openalex.org/W2946303964","https://openalex.org/W2949477454","https://openalex.org/W2962837118","https://openalex.org/W2962914239","https://openalex.org/W2962937599","https://openalex.org/W2963840672","https://openalex.org/W2963993484","https://openalex.org/W2964090697","https://openalex.org/W2964184998","https://openalex.org/W3100715778","https://openalex.org/W3102255912","https://openalex.org/W3154664134","https://openalex.org/W4288076010","https://openalex.org/W4320013936","https://openalex.org/W6637618735","https://openalex.org/W6639824700","https://openalex.org/W6696085341","https://openalex.org/W6755108168","https://openalex.org/W6758428495"],"related_works":["https://openalex.org/W3169126738","https://openalex.org/W1994279415","https://openalex.org/W2558559991","https://openalex.org/W1986338341","https://openalex.org/W2545065926","https://openalex.org/W2980082320","https://openalex.org/W3093839383","https://openalex.org/W2387510934","https://openalex.org/W2015446345","https://openalex.org/W2116510815"],"abstract_inverted_index":{"Supervised":[0],"convolutional":[1],"neural":[2],"networks":[3],"(CNNs)":[4],"have":[5],"demonstrated":[6,173],"state-of-art":[7],"performance":[8,16],"in":[9,39,109,212],"medical":[10,224],"image":[11,225],"segmentation":[12,95,226],"tasks.":[13],"However,":[14],"the":[15,32,57,67,110,117,175,180,183,198,209],"of":[17,41,49,122,182],"a":[18,46,85,126],"well-trained":[19],"CNN":[20,58,68,185],"on":[21,31,134],"an":[22,101],"independent":[23],"dataset":[24,54],"(e.g.,":[25],"different":[26,114],"vendors,":[27],"sequences)":[28],"relies":[29],"strongly":[30],"distribution":[33,42],"similarity,":[34],"and":[35,61,128,166],"may":[36],"drop":[37],"unexpectedly":[38],"case":[40],"shift.":[43],"To":[44],"obtain":[45],"large":[47],"amount":[48],"annotation":[50],"from":[51,70,96,159,187],"each":[52],"new":[53],"for":[55],"re-training":[56],"is":[59,125,141,217],"expensive":[60],"impractical.":[62],"Adaptation":[63],"algorithms":[64],"to":[65,73,105,143,147,190,222],"improve":[66],"generalizability":[69],"source":[71],"domain":[72,75,89,107],"target":[74],"has":[76],"significant":[77],"practical":[78],"value.":[79],"In":[80],"this":[81,123],"work,":[82],"we":[83],"propose":[84,104],"highly":[86],"efficient":[87],"end-to-end":[88],"adaptation":[90,108],"approach,":[91],"with":[92],"left":[93],"ventricle":[94],"cine":[97],"MRI":[98],"sequences":[99],"as":[100,168],"example.":[102],"We":[103],"perform":[106],"output":[111,130],"space":[112],"where":[113],"domains":[115],"share":[116],"strongest":[118],"similarities.":[119],"The":[120,214],"core":[121],"algorithm":[124],"flexible":[127],"light":[129],"adaption":[131,216],"module":[132],"based":[133],"adversarial":[135,150],"learning.":[136,151],"Moreover,":[137,197],"Canny":[138,204],"edge":[139,205,210],"detector":[140,206],"introduced":[142],"enhance":[144],"model's":[145],"attention":[146],"edges":[148],"during":[149],"Comparative":[152],"experiments":[153],"were":[154],"carried":[155],"out":[156],"using":[157],"images":[158],"three":[160,169],"major":[161],"MR":[162],"vendors":[163,192],"(Philips,":[164],"Siemens,":[165],"GE)":[167],"domains.":[170],"Our":[171],"results":[172],"that":[174,202],"proposed":[176,215],"method":[177],"substantially":[178],"improved":[179],"generalization":[181],"trained":[184],"model":[186],"one":[188],"vendor":[189],"other":[191,223],"without":[193],"any":[194],"additional":[195],"annotation.":[196],"ablation":[199],"study":[200],"proved":[201],"introducing":[203],"further":[207],"refined":[208],"detection":[211],"segmentation.":[213],"generic":[218],"can":[219],"be":[220],"extended":[221],"problems.":[227]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
