{"id":"https://openalex.org/W4401246992","doi":"https://doi.org/10.1109/access.2024.3437652","title":"MulA-nnUNet: A Multi-Attention Enhanced nnUNet Framework for 3D Abdominal Multi-Organs Segmentation","display_name":"MulA-nnUNet: A Multi-Attention Enhanced nnUNet Framework for 3D Abdominal Multi-Organs Segmentation","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4401246992","doi":"https://doi.org/10.1109/access.2024.3437652"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3437652","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3437652","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2024.3437652","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5106426001","display_name":"J. Ding","orcid":null},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiashuo Ding","raw_affiliation_strings":["School of Computer Science, Hunan University of Technology, Zhuzhou, China"],"raw_orcid":"https://orcid.org/0009-0000-0515-2730","affiliations":[{"raw_affiliation_string":"School of Computer Science, Hunan University of Technology, Zhuzhou, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035939748","display_name":"Wei Ni","orcid":"https://orcid.org/0009-0007-8629-5135"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Ni","raw_affiliation_strings":["School of Computer Science, Hunan University of Technology, Zhuzhou, China"],"raw_orcid":"https://orcid.org/0009-0007-8629-5135","affiliations":[{"raw_affiliation_string":"School of Computer Science, Hunan University of Technology, Zhuzhou, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109613450","display_name":"Jiahui Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I52180223","display_name":"Hunan Agricultural University","ror":"https://ror.org/01dzed356","country_code":"CN","type":"education","lineage":["https://openalex.org/I52180223"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahui Wan","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha, China","institution_ids":["https://openalex.org/I52180223"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000850188","display_name":"Xiaojun Deng","orcid":"https://orcid.org/0000-0002-0505-0651"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojun Deng","raw_affiliation_strings":["School of Computer Science, Hunan University of Technology, Zhuzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Hunan University of Technology, Zhuzhou, China","institution_ids":["https://openalex.org/I49934816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032952145","display_name":"Lanjun Wan","orcid":"https://orcid.org/0000-0001-7236-3589"},"institutions":[{"id":"https://openalex.org/I49934816","display_name":"Hunan University of Technology","ror":"https://ror.org/04j3vr751","country_code":"CN","type":"education","lineage":["https://openalex.org/I49934816"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lanjun Wan","raw_affiliation_strings":["School of Computer Science, Hunan University of Technology, Zhuzhou, China"],"raw_orcid":"https://orcid.org/0000-0001-7236-3589","affiliations":[{"raw_affiliation_string":"School of Computer Science, Hunan University of Technology, Zhuzhou, China","institution_ids":["https://openalex.org/I49934816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.0975,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.78217084,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"12","issue":null,"first_page":"106658","last_page":"106671"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9984999895095825,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9984999895095825,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8246740102767944},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.597331702709198},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5222442746162415},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5178899765014648},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5126913785934448},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.47713208198547363},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4691511392593384},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4519292712211609},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4198416769504547},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41512343287467957},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4132983684539795},{"id":"https://openalex.org/keywords/computer-engineering","display_name":"Computer engineering","score":0.3257865309715271}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8246740102767944},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.597331702709198},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5222442746162415},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5178899765014648},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5126913785934448},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.47713208198547363},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4691511392593384},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4519292712211609},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4198416769504547},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41512343287467957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4132983684539795},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.3257865309715271},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3437652","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3437652","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8e42c52a01ab444aa1150a4b476d9505","is_oa":true,"landing_page_url":"https://doaj.org/article/8e42c52a01ab444aa1150a4b476d9505","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 106658-106671 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3437652","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3437652","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G582228915","display_name":null,"funder_award_id":"22A0408","funder_id":"https://openalex.org/F4320333642","funder_display_name":"Scientific Research Foundation of Hunan Provincial Education Department"},{"id":"https://openalex.org/G6838465300","display_name":null,"funder_award_id":"2023JJ30217","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320333642","display_name":"Scientific Research Foundation of Hunan Provincial Education Department","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2799142782","https://openalex.org/W2908510526","https://openalex.org/W2962914239","https://openalex.org/W2969262604","https://openalex.org/W3014974815","https://openalex.org/W3031696893","https://openalex.org/W3112701542","https://openalex.org/W3114814504","https://openalex.org/W3127751679","https://openalex.org/W3133531783","https://openalex.org/W3163652268","https://openalex.org/W3192731655","https://openalex.org/W3212386989","https://openalex.org/W4212875960","https://openalex.org/W4287225447","https://openalex.org/W4289489408","https://openalex.org/W4321018109","https://openalex.org/W4321232185","https://openalex.org/W4379031951","https://openalex.org/W4384159609","https://openalex.org/W4385346076","https://openalex.org/W4386232257","https://openalex.org/W4387223558","https://openalex.org/W4387813298","https://openalex.org/W4388430308","https://openalex.org/W4389311473","https://openalex.org/W4392902321","https://openalex.org/W4394785247","https://openalex.org/W6623517193","https://openalex.org/W6754551840","https://openalex.org/W6757817989","https://openalex.org/W6839656003","https://openalex.org/W6850201038","https://openalex.org/W6910555300"],"related_works":["https://openalex.org/W3135697610","https://openalex.org/W4390516098","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W1647606319","https://openalex.org/W2922442631","https://openalex.org/W4391382578"],"abstract_inverted_index":{"In":[0,103],"the":[1,7,23,33,40,49,61,66,72,79,96,119,142,146,154,165,168,172,183,190,200,206,210,227,246,252,296,302,311,324,332],"domain":[2],"of":[3,20,26,51,82,148,178,192,199,226,235,251,279,334],"medical":[4],"image":[5,338],"segmentation,":[6],"nnUNet":[8,113,117],"framework":[9,108,229],"is":[10,109,139,180,202,230],"highly":[11],"respected":[12],"for":[13,85,267,270,274],"its":[14],"excellent":[15],"performance":[16,42],"and":[17,28,71,88,100,123,134,152,174,212,238,272,287,307,328],"wide":[18],"range":[19],"applications.":[21],"However,":[22],"inherent":[24],"bias":[25],"locality":[27],"weight":[29],"sharing":[30],"introduced":[31],"by":[32,182,204,232],"continuous":[34],"convolutional":[35,131],"operations":[36,305],"currently":[37],"used":[38],"limits":[39],"network\u2019s":[41],"in":[43,48,78,209,281,289,319],"modeling":[44],"long-term":[45],"dependencies.":[46,163],"Furthermore,":[47],"process":[50],"implementing":[52],"residual":[53],"links,":[54],"certain":[55],"limitations":[56,75],"are":[57,76,92],"encountered":[58],"due":[59],"to":[60,94,159,188],"substantial":[62],"semantic":[63,169],"discrepancy":[64],"between":[65,171],"encoder\u2019s":[67],"output":[68,176],"feature":[69,86,150,195],"maps":[70],"decoder\u2019s.":[73],"These":[74],"seen":[77],"direct":[80],"application":[81],"skip":[83,193],"connections":[84],"fusion":[87],"gradient":[89],"propagation,":[90],"which":[91,115,186,220,259],"known":[93],"impact":[95],"model\u2019s":[97],"convergence":[98],"speed":[99],"overall":[101],"performance.":[102],"this":[104],"paper,":[105],"a":[106,233],"novel":[107],"presented,":[110],"namely":[111],"Multi-Attention":[112],"(MulA-nnUNet),":[114],"utilizes":[116],"as":[118,323],"foundational":[120],"network":[121],"structure":[122],"integrates":[124],"two":[125],"key":[126],"attention":[127,132,136],"mechanisms:":[128],"large":[129],"kernel":[130],"(LKA)":[133],"pixel":[135],"(PA).":[137],"LKA":[138],"embedded":[140],"within":[141],"deep":[143,155],"encoder,":[144],"maintaining":[145],"effectiveness":[147,225],"shallow":[149],"extraction":[151],"enhancing":[153,331],"neural":[156],"networks\u2019":[157],"ability":[158],"understand":[160],"long-range":[161],"spatial":[162],"At":[164],"same":[166],"time,":[167],"distinction":[170],"encoder":[173,211],"decoder\u2019s":[175],"map":[177],"features":[179],"decreased":[181],"PA":[184],"module,":[185],"helps":[187],"improve":[189],"effect":[191],"connection":[194],"fusion.":[196],"The":[197,224],"complexity":[198],"model":[201,298],"reduced":[203],"replacing":[205],"standard":[207],"convolutions":[208,218],"decoder":[213],"layers":[214],"with":[215,241,264],"depthwise":[216],"separable":[217],"(DS),":[219],"have":[221],"fewer":[222],"parameters.":[223],"proposed":[228],"confirmed":[231],"set":[234],"ablation":[236],"experiments":[237,240],"comparison":[239],"current":[242],"state-of-the-art":[243],"models":[244],"on":[245],"computed":[247],"tomography":[248],"(CT)":[249],"subset":[250],"multimodal":[253],"abdominal":[254,336],"multi-organ":[255,337],"segmentation":[256],"dataset":[257],"(AMOS),":[258],"includes":[260],"500":[261],"CT":[262],"scans,":[263],"350":[265],"scans":[266],"training,":[268],"75":[269,273],"validation,":[271],"testing.":[275],"MulA-nnUNet":[276],"shows":[277],"improvements":[278],"1.1%":[280],"mean":[282,290],"dice":[283],"similarity":[284],"coefficient":[285],"(mDSC)":[286],"1.52%":[288],"intersection":[291],"over":[292,308],"union":[293],"(mIoU),":[294],"while":[295],"baseline":[297],"requires":[299],"5":[300],"times":[301,310],"floating":[303],"point":[304],"(FLOPs)":[306],"7":[309],"parameters":[312],"(Params).":[313],"Additionally,":[314],"it":[315],"demonstrates":[316],"superior":[317],"accuracy":[318,333],"segmenting":[320],"organs":[321],"such":[322],"liver,":[325],"stomach,":[326],"aorta,":[327],"pancreas,":[329],"thereby":[330],"3D":[335],"segmentation.":[339]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
