{"id":"https://openalex.org/W4406460031","doi":"https://doi.org/10.1109/bigdata62323.2024.10825381","title":"Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN","display_name":"Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN","publication_year":2024,"publication_date":"2024-12-15","ids":{"openalex":"https://openalex.org/W4406460031","doi":"https://doi.org/10.1109/bigdata62323.2024.10825381"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata62323.2024.10825381","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","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/A5110397382","display_name":"Andrew Heller","orcid":null},"institutions":[{"id":"https://openalex.org/I168959743","display_name":"University of America","ror":"https://ror.org/03s0c9350","country_code":"US","type":"education","lineage":["https://openalex.org/I168959743"]},{"id":"https://openalex.org/I84470341","display_name":"Catholic University of America","ror":"https://ror.org/047yk3s18","country_code":"US","type":"education","lineage":["https://openalex.org/I84470341"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Andrew Heller","raw_affiliation_strings":["Catholic University of America,Department of Electrical Engineering and Computer Science,Washington DC,USA"],"affiliations":[{"raw_affiliation_string":"Catholic University of America,Department of Electrical Engineering and Computer Science,Washington DC,USA","institution_ids":["https://openalex.org/I168959743","https://openalex.org/I84470341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100673526","display_name":"Andy Liu","orcid":"https://orcid.org/0000-0003-3096-1788"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrew Liu","raw_affiliation_strings":["National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA","institution_ids":["https://openalex.org/I4210155647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059247040","display_name":"Lin\u2010Ching Chang","orcid":"https://orcid.org/0000-0002-7780-5742"},"institutions":[{"id":"https://openalex.org/I168959743","display_name":"University of America","ror":"https://ror.org/03s0c9350","country_code":"US","type":"education","lineage":["https://openalex.org/I168959743"]},{"id":"https://openalex.org/I84470341","display_name":"Catholic University of America","ror":"https://ror.org/047yk3s18","country_code":"US","type":"education","lineage":["https://openalex.org/I84470341"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lin-Ching Chang","raw_affiliation_strings":["Catholic University of America,Department of Electrical Engineering and Computer Science,Washington DC,USA"],"affiliations":[{"raw_affiliation_string":"Catholic University of America,Department of Electrical Engineering and Computer Science,Washington DC,USA","institution_ids":["https://openalex.org/I168959743","https://openalex.org/I84470341"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091761360","display_name":"Gregg Cohen","orcid":"https://orcid.org/0000-0003-3196-6775"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gregg Cohen","raw_affiliation_strings":["National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA","institution_ids":["https://openalex.org/I4210155647"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002626470","display_name":"Elizabeth C. Jones","orcid":"https://orcid.org/0000-0003-4914-8180"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Elizabeth C. Jones","raw_affiliation_strings":["National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA","institution_ids":["https://openalex.org/I4210155647"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5044993271","display_name":"Li\u2010Yueh Hsu","orcid":"https://orcid.org/0000-0002-0826-7290"},"institutions":[{"id":"https://openalex.org/I4210155647","display_name":"National Institutes of Health Clinical Center","ror":"https://ror.org/04vfsmv21","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1299022934","https://openalex.org/I1299303238","https://openalex.org/I4210155647"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li-Yueh Hsu","raw_affiliation_strings":["National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA"],"affiliations":[{"raw_affiliation_string":"National Institutes of Health,Radiology and Imaging Sciences, Clinical Center,Bethesda,MD,USA","institution_ids":["https://openalex.org/I4210155647"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5110397382"],"corresponding_institution_ids":["https://openalex.org/I168959743","https://openalex.org/I84470341"],"apc_list":null,"apc_paid":null,"fwci":0.196,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.52061342,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"8665","last_page":"8667"},"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":0.9969000220298767,"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/T12994","display_name":"Infrared Thermography in Medicine","score":0.9939000010490417,"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":0.5952115654945374},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.5882505774497986},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5242312550544739},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.45663243532180786},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.45519718527793884},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.42903172969818115},{"id":"https://openalex.org/keywords/abdominal-fat","display_name":"Abdominal fat","score":0.4270588159561157},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.38079923391342163},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3741437792778015},{"id":"https://openalex.org/keywords/nuclear-medicine","display_name":"Nuclear medicine","score":0.3506675958633423},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3214211165904999},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11326044797897339}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5952115654945374},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.5882505774497986},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5242312550544739},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.45663243532180786},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.45519718527793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42903172969818115},{"id":"https://openalex.org/C2991684624","wikidata":"https://www.wikidata.org/wiki/Q193583","display_name":"Abdominal fat","level":3,"score":0.4270588159561157},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.38079923391342163},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3741437792778015},{"id":"https://openalex.org/C2989005","wikidata":"https://www.wikidata.org/wiki/Q214963","display_name":"Nuclear medicine","level":1,"score":0.3506675958633423},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3214211165904999},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11326044797897339},{"id":"https://openalex.org/C147583825","wikidata":"https://www.wikidata.org/wiki/Q620876","display_name":"Body weight","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata62323.2024.10825381","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata62323.2024.10825381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Zero hunger","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/2"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2531409750","https://openalex.org/W2734349601","https://openalex.org/W2883342798","https://openalex.org/W2937013683","https://openalex.org/W3174314189","https://openalex.org/W4377288831","https://openalex.org/W4392533280"],"related_works":["https://openalex.org/W2069592018","https://openalex.org/W2075740387","https://openalex.org/W2358990940","https://openalex.org/W2093931120","https://openalex.org/W2329812990","https://openalex.org/W2349116365","https://openalex.org/W3021708704","https://openalex.org/W2257755506","https://openalex.org/W2004231473","https://openalex.org/W1522196789"],"abstract_inverted_index":{"We":[0,26],"evaluate":[1,116],"the":[2,47,62,82,131,143,170,189,197,212],"performance":[3,133,162],"of":[4,40,43,70,85,99,172,199,214],"a":[5,117,126],"deep":[6],"learning":[7],"framework":[8,124],"for":[9,90,163],"segmenting":[10],"abdominal":[11,55,164,193],"fat":[12,165],"and":[13,21,38,45,57,77,88,106,115,138,145,166,181,209],"muscle":[14,107,167],"using":[15],"multi-contrast":[16],"Dixon":[17,35,58,136,179],"magnetic":[18],"resonance":[19],"(MR)":[20],"computed":[22],"tomography":[23],"(CT)":[24],"images.":[25,51,141,184],"aim":[27],"to":[28,113,129],"compare":[29,130],"MR":[30,59,78,140,147,183,205],"image":[31,148,194,201],"segmentation":[32,98,132,161,206],"by":[33],"testing":[34],"fat-only,":[36],"water-only,":[37],"combination":[39],"both":[41,75],"types":[42],"images":[44,73],"comparing":[46],"results":[48,152,187,213],"with":[49,125,169],"CT":[50,56,76,186],"Nineteen":[52],"subjects":[53],"underwent":[54],"imaging":[60],"on":[61],"same":[63],"day.":[64],"For":[65],"each":[66],"participant,":[67],"three":[68],"pairs":[69],"matched":[71],"axial":[72],"from":[74],"were":[79,94,110],"selected":[80],"at":[81],"intervertebral":[83],"levels":[84],"L2-L3,":[86],"L3-L4,":[87],"L4-L5":[89],"analysis.":[91],"References":[92],"labels":[93],"generated":[95],"through":[96],"semi-automated":[97],"subcutaneous":[100],"adipose":[101,104],"tissue,":[102,105],"visceral":[103],"areas.":[108],"They":[109],"then":[111],"used":[112,203],"train":[114],"U-Net":[118],"based":[119],"Convolutional":[120],"Neural":[121],"Network":[122],"(CNN)":[123],"3-fold":[127],"cross-validation":[128],"across":[134],"CT,":[135],"fat-only":[137,144,180],"water-only":[139,146,182],"Combining":[142],"inputs":[149],"produced":[150],"superior":[151],"in":[153,192,204],"all":[154],"labels.":[155],"Our":[156],"study":[157],"demonstrates":[158],"that":[159],"CNN-based":[160],"improves":[168],"inclusion":[171],"additional":[173],"input":[174,200],"channels,":[175],"such":[176],"as":[177],"combining":[178],"While":[185],"represent":[188],"gold":[190],"standard":[191],"segmentation,":[195],"increasing":[196],"number":[198],"channels":[202],"can":[207],"approach,":[208],"even":[210],"match,":[211],"CT.":[215]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
