{"id":"https://openalex.org/W3010906173","doi":"https://doi.org/10.1117/12.2549152","title":"CAI-UNet for segmentation of liver lesion in CT image","display_name":"CAI-UNet for segmentation of liver lesion in CT image","publication_year":2020,"publication_date":"2020-03-10","ids":{"openalex":"https://openalex.org/W3010906173","doi":"https://doi.org/10.1117/12.2549152","mag":"3010906173"},"language":"en","primary_location":{"id":"doi:10.1117/12.2549152","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549152","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/A5018084633","display_name":"Sodam Cheon","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sodam Cheon","raw_affiliation_strings":["SAIHST (Korea, Republic of)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SAIHST (Korea, Republic of)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043897361","display_name":"Ehwa Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Ehwa Yang","raw_affiliation_strings":["Sungkyunkwan Univ. (Korea, Republic of)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106920665","display_name":"Won Jae Lee","orcid":"https://orcid.org/0000-0001-5833-989X"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Won Jae Lee","raw_affiliation_strings":["SAIHST (Korea, Republic of)","Sungkyunkwan Univ. (Korea, Republic of)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"SAIHST (Korea, Republic of)","institution_ids":[]},{"raw_affiliation_string":"Sungkyunkwan Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I848706"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100772905","display_name":"Jae\u2010Hun Kim","orcid":"https://orcid.org/0000-0003-3064-6948"},"institutions":[{"id":"https://openalex.org/I848706","display_name":"Sungkyunkwan University","ror":"https://ror.org/04q78tk20","country_code":"KR","type":"education","lineage":["https://openalex.org/I848706"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jae-Hun Kim","raw_affiliation_strings":["Sungkyunkwan Univ. (Korea, Republic of)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sungkyunkwan Univ. (Korea, Republic of)","institution_ids":["https://openalex.org/I848706"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.8223,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7565023,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"76","last_page":"76"},"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":0.9986000061035156,"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":0.9986000061035156,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9771000146865845,"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":0.9710999727249146,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.693879246711731},{"id":"https://openalex.org/keywords/dice","display_name":"Dice","score":0.6181287169456482},{"id":"https://openalex.org/keywords/lesion","display_name":"Lesion","score":0.6004347801208496},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5682541131973267},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5381153225898743},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5002036094665527},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4957326352596283},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.4920370280742645},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42781710624694824},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34094104170799255},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.33086931705474854},{"id":"https://openalex.org/keywords/pathology","display_name":"Pathology","score":0.18587327003479004},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15086328983306885}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.693879246711731},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.6181287169456482},{"id":"https://openalex.org/C2781156865","wikidata":"https://www.wikidata.org/wiki/Q827023","display_name":"Lesion","level":2,"score":0.6004347801208496},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5682541131973267},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5381153225898743},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5002036094665527},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4957326352596283},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.4920370280742645},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42781710624694824},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34094104170799255},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.33086931705474854},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.18587327003479004},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15086328983306885},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2549152","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2549152","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/W4389060404","https://openalex.org/W2973136608","https://openalex.org/W3012828488","https://openalex.org/W4286233748","https://openalex.org/W4254054209","https://openalex.org/W4385633891","https://openalex.org/W3197954266","https://openalex.org/W4389009345","https://openalex.org/W4200334192","https://openalex.org/W4287691568"],"abstract_inverted_index":{"An":[0],"automatic":[1,42],"segmentation":[2,43,184,195,226],"of":[3,8,17,44,106,135,145,170,227,247],"liver":[4,18,46,153,160,193,228,248],"lesion":[5,47,183,194],"is":[6,54,62,70],"one":[7],"the":[9,30,57,65,96,102,107,124,133,182,219,222,245],"essential":[10],"processes":[11],"for":[12,15,41,225],"computer-aided":[13],"diagnosis":[14],"screening":[16],"diseases.":[19],"In":[20],"this":[21],"study,":[22],"we":[23,140,175],"proposed":[24,238],"an":[25],"in-house,":[26],"reinforced":[27],"U-Net,":[28],"i.e.,":[29],"\u2018CT":[31],"attenuation-integrated":[32],"U-Net":[33,232],"(CAIUNet)\u2019":[34],"as":[35],"a":[36,112,163,177],"new":[37],"deep":[38],"learning":[39],"model":[40],"focal":[45,152],"in":[48,80,95,191],"abdominal":[49,147],"CT":[50,66,81,118,148],"imaging.":[51],"The":[52,60,237],"CAIUNet":[53],"based":[55],"on":[56,64,212],"basic":[58,97],"U-Net.":[59,98],"CAI-UNet":[61,190,220,239],"focused":[63],"attenuation":[67,119],"value,":[68],"which":[69,149],"significant":[71,223],"information":[72],"to":[73,116,131,180,243],"differentiate":[74],"between":[75,104],"healthy":[76],"tissues":[77],"and":[78,111,155,208],"lesions":[79,229],"imaging,":[82],"but":[83],"could":[84,240],"not":[85],"be":[86,241],"directly":[87,101],"included":[88],"after":[89],"passing":[90],"through":[91],"several":[92],"convolutional":[93],"operations":[94],"We":[99,215],"introduced":[100],"connection":[103],"outputs":[105],"last":[108],"3x3":[109],"convolution":[110],"raw":[113],"input":[114],"image":[115],"enhance":[117],"information.":[120],"For":[121,138,172],"training":[122],"CAI-UNet,":[123],"weighted":[125],"dice":[126,199,203],"loss":[127],"function":[128],"was":[129],"used":[130,141,242],"solve":[132],"imbalance":[134],"target":[136],"lesions.":[137],"evaluation,":[139],"LiTS":[142],"challenge":[143],"dataset":[144],"131":[146],"contained":[150],"various":[151],"lesions,":[154],"selected":[156],"90":[157],"sets":[158],"containing":[159],"metastasis":[161],"by":[162],"radiologist":[164],"with":[165,231],"more":[166],"than":[167],"30":[168],"years":[169],"experience.":[171],"statistical":[173],"analysis,":[174],"performed":[176],"paired":[178],"t-test":[179],"compare":[181],"accuracy.":[185],"Our":[186],"results":[187],"showed":[188,221],"that":[189,218],"performing":[192],"yielded":[196],"0.646":[197],"global":[198],"score,":[200,204],"0.543":[201],"subject":[202],"0.568":[205],"specificity":[206],"score":[207,211],"0.651":[209],"precision":[210],"test":[213],"dataset.":[214],"also":[216],"found":[217],"improvement":[224],"compared":[230],"only":[233],"(P":[234],"&lt;":[235],"0.05).":[236],"improve":[244],"detection":[246],"lesion.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
