{"id":"https://openalex.org/W2593228870","doi":"https://doi.org/10.1117/12.2253901","title":"Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks","display_name":"Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks","publication_year":2017,"publication_date":"2017-03-03","ids":{"openalex":"https://openalex.org/W2593228870","doi":"https://doi.org/10.1117/12.2253901","mag":"2593228870"},"language":"en","primary_location":{"id":"doi:10.1117/12.2253901","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2253901","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":"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/A5001497522","display_name":"Liset V\u00e1zquez Romaguera","orcid":"https://orcid.org/0000-0001-9731-6789"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Liset V\u00e1zquez Romaguera","raw_affiliation_strings":["Univ. Federal do Amazonas (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. Federal do Amazonas (Brazil)","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057891403","display_name":"M. G. F. Costa","orcid":"https://orcid.org/0000-0002-6839-1402"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Marly Guimar\u00e3es Fernandes Costa","raw_affiliation_strings":["Univ. Federal do Amazonas (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. Federal do Amazonas (Brazil)","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102022371","display_name":"Francisco Romero","orcid":"https://orcid.org/0000-0002-6101-3871"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Francisco Perdig\u00f3n Romero","raw_affiliation_strings":["Univ. Federal do Amazonas (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. Federal do Amazonas (Brazil)","institution_ids":["https://openalex.org/I62885914"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004818827","display_name":"C\u00edcero Ferreira Fernandes Costa Filho","orcid":"https://orcid.org/0000-0003-3325-5715"},"institutions":[{"id":"https://openalex.org/I62885914","display_name":"Universidade Federal do Amazonas","ror":"https://ror.org/02263ky35","country_code":"BR","type":"education","lineage":["https://openalex.org/I62885914"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Cicero Ferreira Fernandes Costa Filho","raw_affiliation_strings":["Univ. Federal do Amazonas (Brazil)"],"affiliations":[{"raw_affiliation_string":"Univ. Federal do Amazonas (Brazil)","institution_ids":["https://openalex.org/I62885914"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5001497522"],"corresponding_institution_ids":["https://openalex.org/I62885914"],"apc_list":null,"apc_paid":null,"fwci":3.9574,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.93750879,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"10134","issue":null,"first_page":"101342Z","last_page":"101342Z"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9962000250816345,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9962000250816345,"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/T10378","display_name":"Advanced MRI Techniques and Applications","score":0.9943000078201294,"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":0.9879000186920166,"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/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7904617786407471},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7458178997039795},{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.7406049370765686},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7168187499046326},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6662101149559021},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6522929072380066},{"id":"https://openalex.org/keywords/hausdorff-distance","display_name":"Hausdorff distance","score":0.5673568248748779},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.4847692847251892},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4819362163543701},{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.4745829105377197},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.44676673412323},{"id":"https://openalex.org/keywords/ventricle","display_name":"Ventricle","score":0.42886656522750854},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.167234867811203},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.12602385878562927}],"concepts":[{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7904617786407471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7458178997039795},{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.7406049370765686},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7168187499046326},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6662101149559021},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6522929072380066},{"id":"https://openalex.org/C141898687","wikidata":"https://www.wikidata.org/wiki/Q1501997","display_name":"Hausdorff distance","level":2,"score":0.5673568248748779},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.4847692847251892},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4819362163543701},{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.4745829105377197},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.44676673412323},{"id":"https://openalex.org/C2778921608","wikidata":"https://www.wikidata.org/wiki/Q2002035","display_name":"Ventricle","level":2,"score":0.42886656522750854},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.167234867811203},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.12602385878562927}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2253901","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2253901","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":"Good health and well-being","score":0.5899999737739563,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320322994","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Amazonas","ror":"https://ror.org/026d6ma13"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1533861849","https://openalex.org/W1821665499","https://openalex.org/W1903029394","https://openalex.org/W1966192780","https://openalex.org/W1987512289","https://openalex.org/W2059240422","https://openalex.org/W2065768018","https://openalex.org/W2076426701","https://openalex.org/W2109178950","https://openalex.org/W2155893237","https://openalex.org/W2295802084","https://openalex.org/W2337438617","https://openalex.org/W4205182665","https://openalex.org/W6631943919","https://openalex.org/W6641858829","https://openalex.org/W6676550427","https://openalex.org/W6697175316","https://openalex.org/W6703635231"],"related_works":["https://openalex.org/W4254879869","https://openalex.org/W3022576529","https://openalex.org/W2628526247","https://openalex.org/W2596401011","https://openalex.org/W4401519790","https://openalex.org/W2913569734","https://openalex.org/W3107204728","https://openalex.org/W4380986815","https://openalex.org/W3161021928","https://openalex.org/W4362600501"],"abstract_inverted_index":{"According":[0],"to":[1,27,29,74,90,110,123,139],"the":[2,9,40,93],"World":[3],"Health":[4],"Organization,":[5],"cardiovascular":[6,96],"diseases":[7],"are":[8],"leading":[10],"cause":[11],"of":[12,45,53,95,220,226,232,237,243],"death":[13],"worldwide,":[14],"accounting":[15],"for":[16,85],"17.3":[17],"million":[18,33],"deaths":[19],"per":[20,142],"year,":[21],"a":[22,50,64,83,103,126,141,217],"number":[23],"that":[24,70],"is":[25,63,71],"expected":[26],"grow":[28],"more":[30],"than":[31],"23.6":[32],"by":[34],"2030.":[35],"Most":[36],"cardiac":[37,87,132,208],"pathologies":[38],"involve":[39],"left":[41],"ventricle;":[42],"therefore,":[43],"estimation":[44],"several":[46],"functional":[47],"parameters":[48],"from":[49,130,212],"previous":[51],"segmentation":[52,88],"this":[54,99,112],"structure":[55],"can":[56],"be":[57],"helpful":[58],"in":[59,125],"diagnosis.":[60],"Manual":[61],"delineation":[62],"time":[65],"consuming":[66],"and":[67,77,114,136,149,195,222,228,234,239,241,245,252],"tedious":[68],"task":[69],"also":[72],"prone":[73],"high":[75],"intra":[76],"inter-observer":[78],"variability.":[79],"Thus,":[80],"there":[81],"exists":[82],"need":[84],"automated":[86],"method":[89],"help":[91],"facilitate":[92],"diagnosis":[94],"diseases.":[97],"In":[98],"work":[100],"we":[101],"propose":[102],"deep":[104,154],"fully":[105],"convolutional":[106],"neural":[107],"network":[108],"architecture":[109,167],"address":[111],"issue":[113],"assess":[115],"its":[116,146],"performance.":[117],"The":[118,165],"model":[119],"was":[120,151],"trained":[121],"end":[122,124],"supervised":[127],"learning":[128,155],"stage":[129],"whole":[131],"MRI":[133],"images":[134,211],"input":[135],"ground":[137],"truth":[138],"make":[140],"pixel":[143],"classification.":[144],"For":[145],"design,":[147],"development":[148],"experimentation":[150],"used":[152],"Caffe":[153],"framework":[156],"over":[157],"an":[158],"NVidia":[159],"Quadro":[160],"K4200":[161],"Graphics":[162],"Processing":[163],"Unit.":[164],"net":[166],"is:":[168],"Conv64-ReLU":[169],"(2x)":[170,175,180,185],"\u2013":[171,173,176,178,181,183,186,188,190,192],"MaxPooling":[172,177,182],"Conv128-ReLU":[174],"Conv256-ReLU":[179],"Conv512-ReLu-Dropout":[184],"Conv2-ReLU":[187],"Deconv":[189],"Crop":[191],"Softmax.":[193],"Training":[194],"testing":[196],"processes":[197],"were":[198],"carried":[199],"out":[200],"using":[201],"5-fold":[202],"cross":[203],"validation":[204],"with":[205,250],"short":[206],"axis":[207],"magnetic":[209],"resonance":[210],"Sunnybrook":[213],"Database.":[214],"We":[215],"obtained":[216],"Dice":[218],"score":[219],"0.92":[221,238],"0.90,":[223],"Hausdorff":[224],"distance":[225],"4.48":[227],"5.43,":[229],"Jaccard":[230],"index":[231],"0.97":[233],"0.97,":[235],"sensitivity":[236],"0.90":[240],"specificity":[242],"0.99":[244],"0.99,":[246],"overall":[247],"mean":[248],"values":[249],"SGD":[251],"RMSProp,":[253],"respectively.":[254]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":5},{"year":2019,"cited_by_count":8},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}