{"id":"https://openalex.org/W2109710857","doi":"https://doi.org/10.1109/cvpr.2008.4587433","title":"Segmentation of left ventricle from 3D cardiac MR image sequences using a subject-specific dynamical model","display_name":"Segmentation of left ventricle from 3D cardiac MR image sequences using a subject-specific dynamical model","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2109710857","doi":"https://doi.org/10.1109/cvpr.2008.4587433","mag":"2109710857","pmid":"https://pubmed.ncbi.nlm.nih.gov/20052308"},"language":"en","primary_location":{"id":"doi:10.1109/cvpr.2008.4587433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5112108272","display_name":"Yun Zhu","orcid":"https://orcid.org/0009-0008-4616-3039"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yun Zhu","raw_affiliation_strings":["Department of Biomedical Engineering and Diagnostic Radiology, Yale University 310 Cedar Street, New Haven, CT 06520","Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Diagnostic Radiology, Yale University 310 Cedar Street, New Haven, CT 06520","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5026211955","display_name":"Xenophon Papademetris","orcid":"https://orcid.org/0000-0001-7711-3335"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xenophon Papademetris","raw_affiliation_strings":["Department of Biomedical Engineering and Diagnostic Radiology, Yale University, New Heaven, CT, USA","Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Diagnostic Radiology, Yale University, New Heaven, CT, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041557267","display_name":"Albert J. Sinusas","orcid":"https://orcid.org/0000-0003-0972-9589"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Albert Sinusas","raw_affiliation_strings":["Department of Biomedical Engineering and Diagnostic Radiology, Yale University, New Heaven, CT, USA","Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Diagnostic Radiology, Yale University, New Heaven, CT, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046673670","display_name":"James S. Duncan","orcid":"https://orcid.org/0000-0002-5167-9856"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James S. Duncan","raw_affiliation_strings":["Department of Biomedical Engineering and Diagnostic Radiology, Yale University, New Heaven, CT, USA","Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Biomedical Engineering and Diagnostic Radiology, Yale University, New Heaven, CT, USA","institution_ids":["https://openalex.org/I32971472"]},{"raw_affiliation_string":"Dept. of Biomed. Eng. & Diagnostic Radiol., Yale Univ., New Haven, CT","institution_ids":["https://openalex.org/I32971472"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.485,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.90025943,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"2008","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9907000064849854,"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":0.9907000064849854,"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/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.9725000262260437,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T12303","display_name":"Tensor decomposition and applications","score":0.9713000059127808,"subfield":{"id":"https://openalex.org/subfields/2605","display_name":"Computational Mathematics"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7813568115234375},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6631186604499817},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6255736947059631},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5434300303459167},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5079973340034485},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.4934903383255005},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.4204097390174866},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40313559770584106}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7813568115234375},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6631186604499817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6255736947059631},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5434300303459167},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5079973340034485},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.4934903383255005},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.4204097390174866},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40313559770584106},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/cvpr.2008.4587433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvpr.2008.4587433","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE Conference on Computer Vision and Pattern Recognition","raw_type":"proceedings-article"},{"id":"pmid:20052308","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/20052308","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","raw_type":null},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.319.5710","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.319.5710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mplab.ucsd.edu/wp-content/uploads/CVPR2008/Conference/data/papers/093.pdf","raw_type":"text"},{"id":"pmh:oai:europepmc.org:1948125","is_oa":false,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2801445","pdf_url":null,"source":{"id":"https://openalex.org/S4306400806","display_name":"Europe PMC (PubMed Central)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1303153112","host_organization_name":"European Bioinformatics Institute","host_organization_lineage":["https://openalex.org/I1303153112"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1479960696","https://openalex.org/W1509468512","https://openalex.org/W1554944419","https://openalex.org/W2013912476","https://openalex.org/W2032051785","https://openalex.org/W2100779717","https://openalex.org/W2112607495","https://openalex.org/W2113055885","https://openalex.org/W2124632179","https://openalex.org/W2159617609","https://openalex.org/W2171033594","https://openalex.org/W2171790646","https://openalex.org/W4255333169","https://openalex.org/W6628757915","https://openalex.org/W6630535748","https://openalex.org/W6676800495"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W4324315429","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W2112284452","https://openalex.org/W2799953226","https://openalex.org/W1522196789"],"abstract_inverted_index":{"Statistical":[0],"model-based":[1],"segmentation":[2,28,113,161],"of":[3,20,31,43,57,90,108,155],"the":[4,40,54,86,97,101,106,109,112,128,132,136,139,160,163,168,180],"left":[5],"ventricle":[6],"from":[7,131,138],"cardiac":[8,45,58,93],"images":[9],"has":[10],"received":[11],"considerable":[12],"attention":[13],"in":[14,100,115,120],"recent":[15],"years.":[16],"While":[17],"a":[18,44,67,91,116],"variety":[19],"statistical":[21],"models":[22,36,50],"have":[23],"been":[24],"shown":[25],"to":[26,122,149],"improve":[27],"results,":[29],"most":[30],"them":[32],"are":[33],"either":[34],"static":[35],"(SM)":[37],"which":[38,52],"neglect":[39,53],"temporal":[41,77],"coherence":[42],"sequence":[46,94],"or":[47],"generic":[48],"dynamical":[49,69],"(GDM)":[51],"inter-subject":[55,74],"variability":[56,75],"shapes":[59],"and":[60,76,135,170,176],"deformations.":[61],"In":[62],"this":[63],"paper,":[64],"we":[65],"use":[66],"subject-specific":[68],"model":[70],"(SSDM)":[71],"that":[72,159],"handles":[73],"dynamics":[78],"(intra-subject":[79],"variability)":[80],"simultaneously.":[81],"It":[82],"can":[83],"progressively":[84],"identify":[85],"specific":[87],"motion":[88],"patterns":[89],"new":[92],"based":[95,126],"on":[96,127,146],"segmentations":[98],"observed":[99],"past":[102,140],"frames.":[103,141],"We":[104,142],"formulate":[105],"integration":[107],"SSDM":[110,164],"into":[111],"process":[114],"recursive":[117],"Bayesian":[118],"framework":[119],"order":[121],"segment":[123],"each":[124],"frame":[125,134],"intensity":[129],"information":[130],"current":[133],"prediction":[137],"perform":[143],"\"Leave-one-out\"":[144],"test":[145],"32":[147],"sequences":[148],"validate":[150],"our":[151],"approach.":[152],"Quantitative":[153],"analysis":[154],"experimental":[156],"results":[157],"shows":[158],"with":[162,167,179],"outperforms":[165],"those":[166],"SM":[169],"GDM":[171],"by":[172],"having":[173],"better":[174],"global":[175],"local":[177],"consistencies":[178],"manual":[181],"segmentation.":[182]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}