{"id":"https://openalex.org/W4363650238","doi":"https://doi.org/10.1117/12.2653944","title":"Multi-structure segmentation on cardiac MRI using multilayer perceptron mixer network","display_name":"Multi-structure segmentation on cardiac MRI using multilayer perceptron mixer network","publication_year":2023,"publication_date":"2023-04-10","ids":{"openalex":"https://openalex.org/W4363650238","doi":"https://doi.org/10.1117/12.2653944"},"language":"en","primary_location":{"id":"doi:10.1117/12.2653944","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"},"type":"conference-paper","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/A5055677011","display_name":"Shaoyan Pan","orcid":"https://orcid.org/0009-0007-1040-0189"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaoyan Pan","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5086751089","display_name":"Chaoqiong Ma","orcid":"https://orcid.org/0000-0002-2276-0778"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chaoqiong Ma","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081954437","display_name":"Chih\u2010Wei Chang","orcid":"https://orcid.org/0000-0002-3818-4381"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chih-Wei Chang","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029173262","display_name":"Jacob Wynne","orcid":null},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob Wynne","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108367355","display_name":"Justin Roper","orcid":null},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Justin Roper","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100365651","display_name":"Tian Liu","orcid":"https://orcid.org/0000-0002-4587-4399"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tian Liu","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100619090","display_name":"Xiaofeng Yang","orcid":"https://orcid.org/0000-0001-9023-5855"},"institutions":[{"id":"https://openalex.org/I150468666","display_name":"Emory University","ror":"https://ror.org/03czfpz43","country_code":"US","type":"education","lineage":["https://openalex.org/I150468666"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaofeng Yang","raw_affiliation_strings":["Winship Cancer Institute, Emory Univ. (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Winship Cancer Institute, Emory Univ. (United States)","institution_ids":["https://openalex.org/I150468666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150468666"],"apc_list":null,"apc_paid":null,"fwci":0.1214,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.3610297,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"11","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9950000047683716,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9950000047683716,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9944000244140625,"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":0.9908000230789185,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7030376195907593},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6441004872322083},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6141901612281799},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6122958660125732},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5902045369148254},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.5857036113739014},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5385504961013794},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.5245340466499329},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.4699335992336273},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46617794036865234},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.44863787293434143},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4263356029987335},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.42192649841308594},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3288600444793701},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.13274526596069336},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07891464233398438}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7030376195907593},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6441004872322083},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6141901612281799},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6122958660125732},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5902045369148254},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.5857036113739014},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5385504961013794},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.5245340466499329},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.4699335992336273},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46617794036865234},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.44863787293434143},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4263356029987335},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.42192649841308594},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3288600444793701},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.13274526596069336},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07891464233398438},{"id":"https://openalex.org/C24326235","wikidata":"https://www.wikidata.org/wiki/Q126095","display_name":"Electronic engineering","level":1,"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":1,"locations":[{"id":"doi:10.1117/12.2653944","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653944","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Biomedical Applications in Molecular, Structural, and Functional Imaging","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2136359393","https://openalex.org/W4295532600","https://openalex.org/W2063823869","https://openalex.org/W3125779006","https://openalex.org/W2047973478","https://openalex.org/W4383558899","https://openalex.org/W4383558898","https://openalex.org/W2067569035","https://openalex.org/W2090985514","https://openalex.org/W4285299496"],"abstract_inverted_index":{"In":[0],"this":[1],"work,":[2],"we":[3],"propose":[4],"MLP-Vnet,":[5],"a":[6,15,80,287],"token-based":[7],"U-shaped":[8],"multilayer":[9],"linear":[10],"perceptron-mixer":[11],"(MLP-Mixer)":[12],"network,":[13],"incorporating":[14],"convolutional":[16,55],"neural":[17],"network":[18,88,101,171,284],"for":[19,290],"multi-structure":[20],"segmentation":[21,82,266,296],"on":[22,89,181,268],"cardiac":[23,270,294,299],"magnetic":[24],"resonance":[25],"imaging":[26],"(MRI).":[27],"The":[28,97,125,139,158,169,207,251],"proposed":[29,87,170,252],"MLP-Vnet":[30,162,237,253],"is":[31,254],"composed":[32],"of":[33,75,99,106,160,195,198,202,256,260],"an":[34,39],"encoder":[35,51,77],"and":[36,109,116,122,137,152,176,200,226,275,292,301],"decoder.":[37],"Taking":[38],"MRI":[40,271,295],"scan":[41],"as":[42],"input,":[43],"the":[44,50,64,70,76,90,100,107,113,117,131,161,183,187,215,233,236,239,265,269,278],"semantic":[45],"features":[46,66],"are":[47,67],"extracted":[48,65],"by":[49,58,130,144,214],"with":[52,72,165],"one":[53],"early":[54],"block":[56],"followed":[57],"four":[59,166],"consecutive":[60],"MLP-Mixer":[61],"blocks.":[62],"Then,":[63],"passed":[68],"to":[69,78,186,232,263,297],"decoder":[71],"mirrored":[73],"architecture":[74],"form":[79],"N-classes":[81],"map.":[83],"We":[84],"evaluated":[85],"our":[86],"Automated":[91],"Cardiac":[92],"Diagnosis":[93],"Challenge":[94],"(ACDC)":[95],"dataset.":[96],"performance":[98,159],"was":[102,163],"assessed":[103],"in":[104],"terms":[105],"volume-":[108],"surface-based":[110,140],"similarities":[111,127,141,210],"between":[112],"predicted":[114],"contours":[115],"manually":[118],"delineated":[119],"ground-truth":[120],"contours,":[121],"computational":[123],"efficiency.":[124],"volume-based":[126],"were":[128,142,211],"measured":[129,143],"Dice":[132],"score":[133],"coefficient":[134],"(DSC),":[135],"sensitivity,":[136],"precision.":[138],"Hausdorff":[145],"distance":[146,150,156],"(HD),":[147],"mean":[148,154],"surface":[149],"(MSD),":[151],"residual":[153],"square":[155],"(RMSD).":[157],"compared":[164],"state-of-the-art":[167],"networks.":[168,281],"demonstrated":[172,213],"statistically":[173],"superior":[174,177],"DSC":[175,194],"sensitivity":[178,197],"or":[179],"precision":[180,201],"all":[182,205],"three":[184],"structures":[185],"competing":[188,234],"networks":[189],"(p-value":[190],"<":[191],"0.05):":[192],"average":[193,217],"0.904,":[196],"0.908":[199],"0.902":[203],"among":[204],"structures.":[206],"best":[208],"surfaceased":[209],"also":[212],"MLP-Vnet:":[216],"HD":[218],"=":[219,223,228],"3.266":[220],"mm,":[221,225],"MSD":[222],"0.684":[224],"RMSD":[227],"1.487":[229],"mm.":[230],"Compared":[231],"networks,":[235],"showed":[238],"shortest":[240],"training":[241],"time":[242,246],"(7.32":[243],"hours)":[244],"inference":[245],"per":[247],"patient":[248],"(3.12":[249],"seconds).":[250],"capable":[255],"using":[257],"reasonable":[258],"number":[259],"trainable":[261],"parameters":[262],"solve":[264],"task":[267],"scans":[272],"more":[273],"quickly":[274],"accurately":[276],"than":[277],"state-ofthe-":[279],"art":[280],"This":[282],"novel":[283],"could":[285],"be":[286],"promising":[288],"tool":[289],"accurate":[291],"efficient":[293],"assist":[298],"diagnosis":[300],"treatment":[302],"decision":[303],"making.":[304]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}