{"id":"https://openalex.org/W4387251733","doi":"https://doi.org/10.1109/ccis59572.2023.10262930","title":"Multimodal RU-Net: A Segmentation Network Based on Multimodal Cardiac CMR Images","display_name":"Multimodal RU-Net: A Segmentation Network Based on Multimodal Cardiac CMR Images","publication_year":2023,"publication_date":"2023-08-12","ids":{"openalex":"https://openalex.org/W4387251733","doi":"https://doi.org/10.1109/ccis59572.2023.10262930"},"language":"en","primary_location":{"id":"doi:10.1109/ccis59572.2023.10262930","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccis59572.2023.10262930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS)","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/A5102562633","display_name":"Qibin Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I135237710","display_name":"Zhejiang Normal University","ror":"https://ror.org/01vevwk45","country_code":"CN","type":"education","lineage":["https://openalex.org/I135237710"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qibin Hong","raw_affiliation_strings":["Zhejiang Normal University,College of Physics and Electronic Information Engineering,Jinhua,China,321004"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Normal University,College of Physics and Electronic Information Engineering,Jinhua,China,321004","institution_ids":["https://openalex.org/I135237710"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071898642","display_name":"Changjiang Zhang","orcid":"https://orcid.org/0000-0002-2170-3878"},"institutions":[{"id":"https://openalex.org/I82760581","display_name":"Taizhou University","ror":"https://ror.org/04fzhyx73","country_code":"CN","type":"education","lineage":["https://openalex.org/I82760581"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changjiang Zhang","raw_affiliation_strings":["Taizhou University,School of Electronic & Information Engineering (School of Big Data Science),Taizhou,China,318000"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Taizhou University,School of Electronic & Information Engineering (School of Big Data Science),Taizhou,China,318000","institution_ids":["https://openalex.org/I82760581"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1025,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.40627453,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"9901","issue":null,"first_page":"240","last_page":"244"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.996999979019165,"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"}},"topics":[{"id":"https://openalex.org/T12386","display_name":"Advanced X-ray and CT Imaging","score":0.996999979019165,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9952999949455261,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9900000095367432,"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":0.8122130632400513},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7268005609512329},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.703397274017334},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5742513537406921},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5681526064872742},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5460354089736938},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5110970139503479},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44784560799598694},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.41625988483428955},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4152461886405945},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3745419979095459}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8122130632400513},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7268005609512329},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.703397274017334},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5742513537406921},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5681526064872742},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5460354089736938},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5110970139503479},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44784560799598694},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.41625988483428955},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4152461886405945},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3745419979095459},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccis59572.2023.10262930","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/ccis59572.2023.10262930","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 9th International Conference on Cloud Computing and Intelligent Systems (CCIS)","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"},{"id":"https://openalex.org/F4320338465","display_name":"Zhejiang Province Public Welfare Technology Application Research Project","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2525945566","https://openalex.org/W2798122215","https://openalex.org/W2962974533","https://openalex.org/W2995848654","https://openalex.org/W3034552520","https://openalex.org/W3114594573","https://openalex.org/W3114662130","https://openalex.org/W3115696735","https://openalex.org/W3116364459","https://openalex.org/W3116683798","https://openalex.org/W3117601024","https://openalex.org/W3117825257","https://openalex.org/W3201655362","https://openalex.org/W4229032878","https://openalex.org/W6639824700","https://openalex.org/W6640054144","https://openalex.org/W6750469568"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"Cardiac":[0],"magnetic":[1],"resonance":[2],"imaging":[3],"(CMR)":[4],"is":[5,252],"crucial":[6],"for":[7,254],"the":[8,12,16,35,44,48,102,115,120,134,144,149,188,215,223,234],"pathological":[9],"segmentation":[10,25,61,161,181,201,220,256],"of":[11,18,27,37,47,62,122,146,197,237,257],"central":[13],"muscle":[14],"in":[15,90,114,133,148,199,204,233],"diagnosis":[17],"myocardial":[19,63,258],"infarction":[20],"(MI)":[21],"patients.":[22],"The":[23,151,240],"automatic":[24,60,255],"technology":[26],"medical":[28,57],"images":[29],"has":[30,66],"achieved":[31,193,210],"significant":[32],"success":[33],"with":[34],"development":[36],"deep":[38,173],"learning":[39],"technology.":[40],"However,":[41],"due":[42],"to":[43,84,118,140,143,178,214],"low":[45],"contrast":[46],"target":[49],"area":[50],"edges,":[51],"irregular":[52],"lesion":[53],"areas,":[54],"and":[55,87,129,137,175,192,202,206,227],"insufficient":[56],"image":[58,109],"data,":[59],"pathology":[64],"still":[65],"great":[67],"challenges.":[68],"In":[69,95,163],"this":[70,96],"article,":[71],"we":[72,98,165],"propose":[73],"an":[74],"improved":[75],"RU-Net":[76,100,186],"model":[77],"called":[78],"a":[79,194],"Multimodal":[80],"RU-":[81],"-Net.":[82],"Used":[83],"segment":[85],"edema":[86,205],"scar":[88,200,207],"areas":[89],"multimodal":[91,108,185],"cardiac":[92],"CMR":[93],"data.":[94],"network,":[97],"use":[99],"as":[101,170],"basic":[103],"model,":[104],"embed":[105],"our":[106,228,249],"proposed":[107,229,250],"feature":[110],"extraction":[111,121],"module":[112],"(MFF)":[113],"encoding":[116,138],"path":[117,139],"enhance":[119,141],"complementary":[123],"information":[124],"between":[125],"different":[126],"modal":[127],"images,":[128],"add":[130],"attention":[131,142],"modules":[132,156],"skip":[135],"connection":[136],"regions":[145],"interest":[147],"image.":[150],"experiment":[152],"shows":[153],"that":[154,248],"both":[155],"mentioned":[157],"above":[158],"effectively":[159],"improve":[160,180],"accuracy.":[162,182],"addition,":[164],"have":[166],"adopted":[167],"methods":[168,221],"such":[169],"data":[171],"augmentation,":[172],"supervision,":[174],"combination":[176],"loss":[177],"further":[179],"We":[183,209],"evaluated":[184],"on":[187,222],"MyoPS2020":[189],"challenge":[190],"dataset":[191],"Dice":[195],"score":[196],"64.4%":[198],"70.7%":[203],"segmentation.":[208],"almost":[211],"equivalent":[212],"performance":[213],"most":[216],"advanced":[217],"single":[218],"stage":[219],"MyoPS":[224],"2020":[225],"ranking,":[226],"method":[230,251],"outperformed":[231],"it":[232],"standard":[235],"deviation":[236],"dice":[238],"scores,":[239],"test":[241],"results":[242],"are":[243],"more":[244],"stable.":[245],"This":[246],"indicates":[247],"meaningful":[253],"pathology.":[259]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}