{"id":"https://openalex.org/W2914545845","doi":"https://doi.org/10.1109/cisp-bmei.2018.8633185","title":"A Multi-Scope Convolutional Neural Network for Automatic Left Ventricle Segmentation from Magnetic Resonance Images: Deep-Learning at Multiple Scopes","display_name":"A Multi-Scope Convolutional Neural Network for Automatic Left Ventricle Segmentation from Magnetic Resonance Images: Deep-Learning at Multiple Scopes","publication_year":2018,"publication_date":"2018-10-01","ids":{"openalex":"https://openalex.org/W2914545845","doi":"https://doi.org/10.1109/cisp-bmei.2018.8633185","mag":"2914545845"},"language":"en","primary_location":{"id":"doi:10.1109/cisp-bmei.2018.8633185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2018.8633185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","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/A5100370295","display_name":"Xinyi Li","orcid":"https://orcid.org/0000-0003-4605-1367"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinyi Li","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100423182","display_name":"Yuanyuan Wang","orcid":"https://orcid.org/0000-0003-1984-1136"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanyuan Wang","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100652053","display_name":"Wenjun Yan","orcid":"https://orcid.org/0000-0002-6055-8677"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenjun Yan","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048648478","display_name":"Rob J. van der Geest","orcid":"https://orcid.org/0000-0002-9084-5597"},"institutions":[{"id":"https://openalex.org/I2800006345","display_name":"Leiden University Medical Center","ror":"https://ror.org/05xvt9f17","country_code":"NL","type":"funder","lineage":["https://openalex.org/I2800006345"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Rob J. Van der Geest","raw_affiliation_strings":["Leiden University Medical Center, Leiden, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leiden University Medical Center, Leiden, The Netherlands","institution_ids":["https://openalex.org/I2800006345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074707604","display_name":"Zeju Li","orcid":"https://orcid.org/0000-0002-4608-2959"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeju Li","raw_affiliation_strings":["Department of Electrical Engineering, Fudan University, Shanghai, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering, Fudan University, Shanghai, China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5028217553","display_name":"Qian Tao","orcid":"https://orcid.org/0000-0001-7480-0703"},"institutions":[{"id":"https://openalex.org/I2800006345","display_name":"Leiden University Medical Center","ror":"https://ror.org/05xvt9f17","country_code":"NL","type":"funder","lineage":["https://openalex.org/I2800006345"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Qian Tao","raw_affiliation_strings":["Leiden University Medical Center, Leiden, The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leiden University Medical Center, Leiden, The Netherlands","institution_ids":["https://openalex.org/I2800006345"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9944999814033508,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9944999814033508,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9904000163078308,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9878000020980835,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.8040862679481506},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7030754089355469},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6787723302841187},{"id":"https://openalex.org/keywords/scope","display_name":"Scope (computer science)","score":0.6575616002082825},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6468565464019775},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6030933856964111},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.5973255634307861},{"id":"https://openalex.org/keywords/ventricle","display_name":"Ventricle","score":0.48885005712509155},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4407894015312195},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4382704794406891},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.38676005601882935},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36980634927749634},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.08026289939880371},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.07243478298187256}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.8040862679481506},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7030754089355469},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6787723302841187},{"id":"https://openalex.org/C2778012447","wikidata":"https://www.wikidata.org/wiki/Q1034415","display_name":"Scope (computer science)","level":2,"score":0.6575616002082825},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6468565464019775},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6030933856964111},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.5973255634307861},{"id":"https://openalex.org/C2778921608","wikidata":"https://www.wikidata.org/wiki/Q2002035","display_name":"Ventricle","level":2,"score":0.48885005712509155},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4407894015312195},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4382704794406891},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.38676005601882935},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36980634927749634},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.08026289939880371},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.07243478298187256},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cisp-bmei.2018.8633185","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cisp-bmei.2018.8633185","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1977532005","https://openalex.org/W1987512289","https://openalex.org/W2059240422","https://openalex.org/W2076426701","https://openalex.org/W2092465019","https://openalex.org/W2223158884","https://openalex.org/W2322371438","https://openalex.org/W2345010043","https://openalex.org/W2777186991","https://openalex.org/W2919115771"],"related_works":["https://openalex.org/W4293211451","https://openalex.org/W3102253946","https://openalex.org/W2795329967","https://openalex.org/W4226289457","https://openalex.org/W3144574764","https://openalex.org/W3156786002","https://openalex.org/W1669643531","https://openalex.org/W2122581818","https://openalex.org/W2948658236","https://openalex.org/W2738221750"],"abstract_inverted_index":{"Cardiac":[0],"Magnetic":[1],"Resonance":[2],"(CMR)":[3],"imaging":[4],"is":[5,27],"widely":[6],"used":[7],"in":[8,99],"the":[9,13,20,24,42,69,86,90,96,105,116,119,128,142,154,167,173],"clinic":[10],"to":[11,67,84,164,185],"assess":[12],"patient-specific":[14],"cardiac":[15],"structure":[16],"and":[17,29,108],"function.":[18],"However,":[19],"manual":[21],"analysis":[22],"of":[23,54,72,75,95,137,149],"CMR":[25],"data":[26],"tedious":[28],"subjective.":[30],"In":[31],"this":[32],"work,":[33],"we":[34],"developed":[35],"a":[36,59,78,132,178],"fully":[37],"automatic":[38],"segmentation":[39,147],"system":[40,52],"for":[41,181],"left":[43],"ventricle":[44],"(LV)":[45],"myocardium":[46,88],"from":[47,89,169],"MR":[48,139,170],"cine":[49,140],"images.":[50],"The":[51,158],"consists":[53],"three":[55],"major":[56],"components.":[57],"Firstly,":[58],"conventional":[60],"convolutional":[61],"neural":[62],"network":[63],"(CNN)":[64],"was":[65,82],"trained":[66],"detect":[68],"global":[70,109],"region":[71],"interest":[73],"(ROI)":[74],"LV.":[76],"Secondly,":[77],"novel":[79],"multi-scope":[80],"CNN":[81],"proposed":[83,159],"segment":[85,166],"LV":[87,168],"reduced":[91],"ROI,":[92],"taking":[93],"advantage":[94],"image":[97],"context":[98],"different":[100],"scopes,":[101],"such":[102],"that":[103],"both":[104],"local":[106,182],"accuracy":[107,148],"consistency":[110],"can":[111,161],"be":[112,162],"implicitly":[113],"learned":[114],"by":[115,153],"CNN.":[117],"Finally":[118],"results":[120],"were":[121],"pruned":[122],"with":[123,172],"simple":[124],"morphological":[125],"filtering":[126],"preserving":[127],"largest":[129],"component.":[130],"With":[131],"relatively":[133],"small":[134],"training":[135],"set":[136],"200":[138],"images,":[141],"method":[143,160],"achieved":[144],"an":[145],"average":[146],"0.71":[150],"as":[151,177],"expressed":[152],"Dice":[155],"overlap":[156],"index.":[157],"applied":[163],"automatically":[165],"images":[171],"reasonable":[174],"accuracy,":[175],"or":[176],"proper":[179],"initialization":[180],"shape":[183],"methods":[184],"achieve":[186],"further":[187],"refined":[188],"results.":[189]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
