{"id":"https://openalex.org/W2300825141","doi":"https://doi.org/10.1117/12.2217883","title":"A comparison study of atlas-based 3D cardiac MRI segmentation: global versus global and local transformations","display_name":"A comparison study of atlas-based 3D cardiac MRI segmentation: global versus global and local transformations","publication_year":2016,"publication_date":"2016-03-18","ids":{"openalex":"https://openalex.org/W2300825141","doi":"https://doi.org/10.1117/12.2217883","mag":"2300825141"},"language":"en","primary_location":{"id":"doi:10.1117/12.2217883","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2217883","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","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/A5088938774","display_name":"Aditya Daryanani","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aditya Daryanani","raw_affiliation_strings":["Rochester Institute of Technology (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology (United States)","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085170120","display_name":"Shusil Dangi","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shusil Dangi","raw_affiliation_strings":["Rochester Institute of Technology (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology (United States)","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003961290","display_name":"Yehuda K. Ben-Zikri","orcid":null},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yehuda Kfir Ben-Zikri","raw_affiliation_strings":["Rochester Institute of Technology (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology (United States)","institution_ids":["https://openalex.org/I155173764"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085736904","display_name":"Cristian A. Linte","orcid":"https://orcid.org/0000-0001-7602-7937"},"institutions":[{"id":"https://openalex.org/I155173764","display_name":"Rochester Institute of Technology","ror":"https://ror.org/00v4yb702","country_code":"US","type":"education","lineage":["https://openalex.org/I155173764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cristian A. Linte","raw_affiliation_strings":["Rochester Institute of Technology (United States)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rochester Institute of Technology (United States)","institution_ids":["https://openalex.org/I155173764"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I155173764"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9786","issue":null,"first_page":"978624","last_page":"978624"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9997000098228455,"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":0.9997000098228455,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9987000226974487,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9958000183105469,"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/jaccard-index","display_name":"Jaccard index","score":0.7327472567558289},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7224673628807068},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6944789290428162},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.6859568357467651},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6803773641586304},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6219686269760132},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5919925570487976},{"id":"https://openalex.org/keywords/cut","display_name":"Cut","score":0.5411376953125},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.499814510345459},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.48480239510536194},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.429569810628891},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42332160472869873},{"id":"https://openalex.org/keywords/cardiac-magnetic-resonance-imaging","display_name":"Cardiac magnetic resonance imaging","score":0.4131559729576111},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3640190660953522},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.18543076515197754},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.1822945475578308},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1324726641178131}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.7327472567558289},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7224673628807068},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6944789290428162},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.6859568357467651},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6803773641586304},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6219686269760132},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5919925570487976},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.5411376953125},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.499814510345459},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.48480239510536194},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.429569810628891},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42332160472869873},{"id":"https://openalex.org/C2776008845","wikidata":"https://www.wikidata.org/wiki/Q5038325","display_name":"Cardiac magnetic resonance imaging","level":3,"score":0.4131559729576111},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3640190660953522},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.18543076515197754},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.1822945475578308},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1324726641178131}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2217883","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2217883","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","score":0.4300000071525574,"id":"https://metadata.un.org/sdg/17"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1491198555","https://openalex.org/W1571998429","https://openalex.org/W1987512289","https://openalex.org/W1998070036","https://openalex.org/W2079158221","https://openalex.org/W2109907633","https://openalex.org/W2113576511","https://openalex.org/W2303225500","https://openalex.org/W4248217200","https://openalex.org/W6629273509","https://openalex.org/W6698283192"],"related_works":["https://openalex.org/W2953570019","https://openalex.org/W3012828488","https://openalex.org/W3116883888","https://openalex.org/W4367019122","https://openalex.org/W3161021928","https://openalex.org/W4287631720","https://openalex.org/W4280645644","https://openalex.org/W2766422710","https://openalex.org/W3093926553","https://openalex.org/W4380986815"],"abstract_inverted_index":{"Magnetic":[0],"Resonance":[1],"Imaging":[2],"(MRI)":[3],"is":[4,90,132],"a":[5,135,139,172],"standard-of-care":[6],"imaging":[7],"modality":[8],"for":[9,93],"cardiac":[10,16,54],"function":[11],"assessment":[12],"and":[13,24,44,85,143,178,199,204,227],"guidance":[14,95],"of":[15,26,71,83,103,116,119,141,150,167,174],"interventions":[17],"thanks":[18],"to":[19,28,67,73,134,155,215],"its":[20],"high":[21,147],"image":[22,124,137],"quality":[23],"lack":[25],"exposure":[27],"ionizing":[29],"radiation.":[30],"Cardiac":[31],"health":[32],"parameters":[33],"such":[34],"as":[35],"left":[36,169],"ventricular":[37],"volume,":[38],"ejection":[39],"fraction,":[40],"myocardial":[41],"mass,":[42],"thickness,":[43],"strain":[45],"can":[46,64],"be":[47,65,74],"assessed":[48],"by":[49],"segmenting":[50],"the":[51,58,81,100,108,117,127,168,193,197,211],"heart":[52,62],"from":[53,121,171,196],"MRI":[55,176],"images.":[56],"Furthermore,":[57],"segmented":[59],"pre-operative":[60],"anatomical":[61],"models":[63],"used":[66],"precisely":[68],"identify":[69],"regions":[70],"interest":[72,120],"treated":[75],"during":[76],"minimally":[77],"invasive":[78],"therapy.":[79],"Hence,":[80],"use":[82],"accurate":[84],"computationally":[86],"efficient":[87],"segmentation":[88,102,112,159],"techniques":[89,209],"critical,":[91],"especially":[92],"intra-procedural":[94],"applications":[96],"that":[97],"rely":[98],"on":[99,164,186],"peri-operative":[101],"subject-specific":[104],"datasets":[105],"without":[106],"delaying":[107],"procedure":[109],"workflow.":[110],"Atlas-based":[111],"incorporates":[113],"prior":[114],"knowledge":[115],"anatomy":[118],"expertly":[122],"annotated":[123],"datasets.":[125],"Typically,":[126],"ground":[128],"truth":[129],"atlas":[130,166],"label":[131],"propagated":[133],"test":[136],"using":[138,160,181,206],"combination":[140],"global":[142,161,198,200],"local":[144,202],"registration.":[145],"The":[146],"computational":[148],"cost":[149],"non-rigid":[151],"registration":[152],"motivated":[153],"us":[154],"obtain":[156],"an":[157,165],"initial":[158],"transformations":[162],"based":[163,185],"ventricle":[170],"population":[173],"patient":[175],"images":[177],"refine":[179],"it":[180],"well":[182],"developed":[183],"technique":[184],"graph":[187,207],"cuts.":[188],"Here":[189],"we":[190],"quantitatively":[191],"compare":[192],"segmentations":[194,213],"obtained":[195],"plus":[201],"atlases":[203],"refined":[205],"cut-based":[208],"with":[210],"expert":[212],"according":[214],"several":[216],"similarity":[217],"metrics,":[218],"including":[219],"Dice":[220],"correlation":[221],"coefficient,":[222,224],"Jaccard":[223],"Hausdorff":[225],"distance,":[226],"Mean":[228],"absolute":[229],"distance":[230],"error.":[231]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}