{"id":"https://openalex.org/W2524916860","doi":"https://doi.org/10.1007/978-3-319-46726-9_31","title":"Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network","display_name":"Recognizing End-Diastole and End-Systole Frames via Deep Temporal Regression Network","publication_year":2016,"publication_date":"2016-01-01","ids":{"openalex":"https://openalex.org/W2524916860","doi":"https://doi.org/10.1007/978-3-319-46726-9_31","mag":"2524916860"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-46726-9_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-46726-9_31","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"book-chapter","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1007/978-3-319-46726-9_31","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101560782","display_name":"Bin Kong","orcid":null},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Bin Kong","raw_affiliation_strings":["Department of Computer Science, UNC Charlotte, Charlotte, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, UNC Charlotte, Charlotte, North Carolina, USA","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062819602","display_name":"Yiqiang Zhan","orcid":"https://orcid.org/0000-0001-8391-2555"},"institutions":[{"id":"https://openalex.org/I4210151799","display_name":"Siemens Healthcare (United States)","ror":"https://ror.org/054962n91","country_code":"US","type":"company","lineage":["https://openalex.org/I4210151799"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yiqiang Zhan","raw_affiliation_strings":["Siemens Healthcare, Malvern, Pennsylvania, USA"],"affiliations":[{"raw_affiliation_string":"Siemens Healthcare, Malvern, Pennsylvania, USA","institution_ids":["https://openalex.org/I4210151799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037102683","display_name":"Min Chul Shin","orcid":"https://orcid.org/0000-0002-3296-2357"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Min Shin","raw_affiliation_strings":["Department of Computer Science, UNC Charlotte, Charlotte, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, UNC Charlotte, Charlotte, North Carolina, USA","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010977815","display_name":"Thomas N. Denny","orcid":"https://orcid.org/0000-0002-7364-8276"},"institutions":[{"id":"https://openalex.org/I82497590","display_name":"Auburn University","ror":"https://ror.org/02v80fc35","country_code":"US","type":"education","lineage":["https://openalex.org/I82497590"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Thomas Denny","raw_affiliation_strings":["MRI Research Center, Auburn University, Auburn, Alabama, USA"],"affiliations":[{"raw_affiliation_string":"MRI Research Center, Auburn University, Auburn, Alabama, USA","institution_ids":["https://openalex.org/I82497590"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066553616","display_name":"Shaoting Zhang","orcid":"https://orcid.org/0000-0002-8719-448X"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaoting Zhang","raw_affiliation_strings":["Department of Computer Science, UNC Charlotte, Charlotte, North Carolina, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, UNC Charlotte, Charlotte, North Carolina, USA","institution_ids":["https://openalex.org/I102149020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5101560782"],"corresponding_institution_ids":["https://openalex.org/I102149020"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":{"value":5000,"currency":"EUR","value_usd":5392},"fwci":80.6398,"has_fulltext":false,"cited_by_count":108,"citation_normalized_percentile":{"value":0.99964652,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"264","last_page":"272"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.9915000200271606,"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"}},"topics":[{"id":"https://openalex.org/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.9915000200271606,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.978600025177002,"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/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.9678999781608582,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8060763478279114},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7093859314918518},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7043406963348389},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5614933371543884},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.555835485458374},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.516451358795166},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4601358473300934},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.45108917355537415},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39271867275238037},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38852763175964355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8060763478279114},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7093859314918518},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7043406963348389},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5614933371543884},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.555835485458374},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.516451358795166},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4601358473300934},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.45108917355537415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39271867275238037},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38852763175964355},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-319-46726-9_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-46726-9_31","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":{"id":"doi:10.1007/978-3-319-46726-9_31","is_oa":true,"landing_page_url":"https://doi.org/10.1007/978-3-319-46726-9_31","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1038736503","https://openalex.org/W1534785737","https://openalex.org/W1573946964","https://openalex.org/W1581407678","https://openalex.org/W1590321502","https://openalex.org/W1908709347","https://openalex.org/W1947481528","https://openalex.org/W1998363540","https://openalex.org/W2013204948","https://openalex.org/W2044846603","https://openalex.org/W2082526668","https://openalex.org/W2117420666","https://openalex.org/W2117539524","https://openalex.org/W2155893237","https://openalex.org/W2294318823","https://openalex.org/W2329583406","https://openalex.org/W2345010043","https://openalex.org/W2949667497","https://openalex.org/W6634906388"],"related_works":["https://openalex.org/W4225394202","https://openalex.org/W4298287631","https://openalex.org/W2953061907","https://openalex.org/W1847088711","https://openalex.org/W3036642985","https://openalex.org/W4226493464","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3008584592"],"abstract_inverted_index":{"Accurate":[0],"measurement":[1],"of":[2,13,19,30,52,65,96,118,181,207,222],"left":[3,57],"ventricular":[4],"volumes":[5],"and":[6,27,46,67,107,121,185,225],"Ejection":[7],"Fraction":[8],"from":[9,70,104,158],"cine":[10],"MRI":[11,159],"is":[12,73,77,89,217,229],"paramount":[14],"importance":[15],"to":[16,33,93,153,203],"the":[17,56,63,82,94,115,122,163,169,178,189,205,213,226],"evaluation":[18],"cardiovascular":[20],"functions,":[21],"yet":[22],"it":[23],"usually":[24],"requires":[25],"laborious":[26],"tedious":[28],"work":[29],"trained":[31],"experts":[32],"interpret":[34],"them.":[35],"To":[36],"facilitate":[37],"this":[38,138],"procedure,":[39],"numerous":[40],"computer":[41],"aided":[42],"diagnosis":[43],"(CAD)":[44],"methods":[45,128],"tools":[47],"have":[48,129],"been":[49,130],"proposed,":[50],"most":[51],"which":[53,76,210],"focus":[54],"on":[55,220],"or":[58],"right":[59],"ventricle":[60],"segmentation.":[61],"However,":[62],"identification":[64],"ES":[66],"ED":[68],"frames":[69,103,157],"cardiac":[71,183,223],"sequences":[72,224],"largely":[74],"ignored,":[75],"a":[78,105,142,175,182,186,196],"key":[79],"procedure":[80],"in":[81,133,200],"automated":[83],"workflow.":[84],"This":[85],"seemingly":[86],"easy":[87],"task":[88],"quite":[90],"challenging,":[91],"due":[92],"requirement":[95],"high":[97],"accuracy":[98],"(i.e.,":[99],"precisely":[100],"identifying":[101],"specific":[102,156],"sequence)":[106],"subtle":[108],"differences":[109],"among":[110],"consecutive":[111],"frames.":[112],"Recently,":[113],"with":[114,168,235],"rapid":[116],"growth":[117],"annotated":[119],"data":[120],"increasing":[123],"computational":[124],"power,":[125],"deep":[126,144],"learning":[127,145],"widely":[131],"exploited":[132],"medical":[134],"image":[135],"analysis.":[136],"In":[137,192],"paper,":[139],"we":[140,194],"propose":[141],"novel":[143],"architecture,":[146],"named":[147],"as":[148],"temporal":[149,190],"regression":[150],"network":[151,202],"(TempReg-Net),":[152],"accurately":[154],"identify":[155],"sequences,":[160],"by":[161],"integrating":[162],"Convolutional":[164],"Neural":[165,171],"Network":[166,172],"(CNN)":[167],"Recurrent":[170],"(RNN).":[173],"Specifically,":[174],"CNN":[176],"encodes":[177],"spatial":[179],"information":[180],"sequence,":[184],"RNN":[187],"decodes":[188],"information.":[191],"addition,":[193],"design":[195],"new":[197],"loss":[198],"function":[199],"our":[201],"constrain":[204],"structure":[206],"predicted":[208],"labels,":[209],"further":[211],"improves":[212],"performance.":[214],"Our":[215],"approach":[216],"extensively":[218],"validated":[219],"thousands":[221],"average":[227],"difference":[228],"merely":[230],"0.4":[231],"frames,":[232],"comparing":[233],"favorably":[234],"previous":[236],"systems.":[237]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":15},{"year":2019,"cited_by_count":18},{"year":2018,"cited_by_count":23},{"year":2017,"cited_by_count":10},{"year":2016,"cited_by_count":2}],"updated_date":"2026-03-25T14:56:36.534964","created_date":"2025-10-10T00:00:00"}
