{"id":"https://openalex.org/W2326989241","doi":"https://doi.org/10.1109/tase.2015.2403372","title":"An Efficient and Robust Method for Automatically Identifying the Left Ventricular Boundary in Cine Magnetic Resonance Images","display_name":"An Efficient and Robust Method for Automatically Identifying the Left Ventricular Boundary in Cine Magnetic Resonance Images","publication_year":2015,"publication_date":"2015-02-27","ids":{"openalex":"https://openalex.org/W2326989241","doi":"https://doi.org/10.1109/tase.2015.2403372","mag":"2326989241"},"language":"en","primary_location":{"id":"doi:10.1109/tase.2015.2403372","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2015.2403372","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Automation Science and Engineering","raw_type":"journal-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/A5109572692","display_name":"Zhenzhou Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I142078773","display_name":"Shenyang Institute of Automation","ror":"https://ror.org/00ft6nj33","country_code":"CN","type":"facility","lineage":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I143302722","display_name":"University of Kentucky","ror":"https://ror.org/02k3smh20","country_code":"US","type":"education","lineage":["https://openalex.org/I143302722"]}],"countries":["CN","US"],"is_corresponding":true,"raw_author_name":"ZhenZhou Wang","raw_affiliation_strings":["Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang City, Liaoning Province, P.R. China","University of Kentucky"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Shenyang Institute of Automation, Shenyang City, Liaoning Province, P.R. China","institution_ids":["https://openalex.org/I142078773","https://openalex.org/I19820366"]},{"raw_affiliation_string":"University of Kentucky","institution_ids":["https://openalex.org/I143302722"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5109572692"],"corresponding_institution_ids":["https://openalex.org/I142078773","https://openalex.org/I143302722","https://openalex.org/I19820366"],"apc_list":null,"apc_paid":null,"fwci":2.1077,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.86935968,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"13","issue":"2","first_page":"536","last_page":"542"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9948999881744385,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9948999881744385,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9840999841690063,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9801999926567078,"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/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.5395208597183228},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5335012078285217},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.5184916257858276},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48289012908935547},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.43359148502349854},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.32269543409347534},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29071491956710815},{"id":"https://openalex.org/keywords/mathematical-analysis","display_name":"Mathematical analysis","score":0.2313290536403656},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15072059631347656},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.1439324915409088}],"concepts":[{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.5395208597183228},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5335012078285217},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.5184916257858276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48289012908935547},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.43359148502349854},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.32269543409347534},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29071491956710815},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.2313290536403656},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15072059631347656},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.1439324915409088}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tase.2015.2403372","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tase.2015.2403372","pdf_url":null,"source":{"id":"https://openalex.org/S34881539","display_name":"IEEE Transactions on Automation Science and Engineering","issn_l":"1545-5955","issn":["1545-5955","1558-3783"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Automation Science and Engineering","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1987512289","https://openalex.org/W1998223127","https://openalex.org/W2023093778","https://openalex.org/W2042084395","https://openalex.org/W2060128769","https://openalex.org/W2099638419","https://openalex.org/W2105259808","https://openalex.org/W2105687765","https://openalex.org/W2118877769","https://openalex.org/W2121213023","https://openalex.org/W2135981539","https://openalex.org/W2146308017","https://openalex.org/W2150454786","https://openalex.org/W2155389906","https://openalex.org/W2156290174","https://openalex.org/W2157273833","https://openalex.org/W2162007043","https://openalex.org/W2163960877","https://openalex.org/W2165680220","https://openalex.org/W6677548441"],"related_works":["https://openalex.org/W2755342338","https://openalex.org/W2058170566","https://openalex.org/W2036807459","https://openalex.org/W2772917594","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2079911747"],"abstract_inverted_index":{"Efficient":[0],"and":[1,28,57,88,101],"robust":[2,102],"identification":[3],"of":[4,46],"the":[5,25,44,63,67,95,104],"left":[6,26],"ventricular":[7],"borders":[8,32],"remains":[9],"a":[10],"challenging":[11],"problem":[12],"in":[13,106],"cardiology.":[14],"In":[15],"this":[16],"paper,":[17],"we":[18],"proposed":[19,35,64,96],"an":[20],"automatic":[21],"method":[22,36,65,71,97],"to":[23],"segment":[24],"ventricles":[27],"then":[29],"identify":[30],"their":[31],"robustly.":[33],"The":[34],"is":[37,98],"named":[38],"as":[39],"\u201cABDC\u201d":[40],"because":[41],"it":[42,92],"utilizes":[43],"strengths":[45],"four":[47],"techniques:":[48],"Automatic":[49],"threshold":[50],"selection;":[51],"Boundary":[52],"extraction,":[53],"Deformation":[54],"flow":[55,70],"tracking,":[56],"Convex":[58],"shape":[59],"modeling.":[60],"We":[61],"compared":[62],"with":[66],"PDE":[68],"optical":[69],"on":[72],"1660":[73],"images":[74],"which":[75],"are":[76],"obtained":[77],"from":[78],"ten":[79],"complete":[80],"short-axis":[81],"cine":[82],"MRI":[83],"datasets":[84],"(five":[85],"normal":[86],"subjects":[87],"five":[89],"patients).":[90],"As":[91],"turned":[93],"out,":[94],"more":[99],"efficient":[100],"than":[103],"benchmark":[105],"segmenting":[107],"LV":[108],"borders.":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
