{"id":"https://openalex.org/W4211213166","doi":"https://doi.org/10.1109/fit53504.2021.00029","title":"Automated Localization and Segmentation of Left Ventricle in Cardiac MRI using Faster R-CNN","display_name":"Automated Localization and Segmentation of Left Ventricle in Cardiac MRI using Faster R-CNN","publication_year":2021,"publication_date":"2021-12-01","ids":{"openalex":"https://openalex.org/W4211213166","doi":"https://doi.org/10.1109/fit53504.2021.00029"},"language":"en","primary_location":{"id":"doi:10.1109/fit53504.2021.00029","doi":"https://doi.org/10.1109/fit53504.2021.00029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fit53504.2021.00029","pdf_url":null,"source":{"id":"https://openalex.org/S7407087284","display_name":"International Conference on Frontiers of Information Technology (FIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Frontiers of Information Technology (FIT)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5029594206","display_name":"Nauman Qadeer","orcid":"https://orcid.org/0000-0002-2178-0435","observed_orcids":["https://orcid.org/0000-0002-2178-0435"]},"institutions":[{"id":"https://openalex.org/I16076960","display_name":"COMSATS University Islamabad","ror":"https://ror.org/00nqqvk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I16076960"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Nauman Qadeer","raw_affiliation_strings":["COMSATS University Islamabad,Department of Computer Sciences,Wah Campus,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"COMSATS University Islamabad,Department of Computer Sciences,Wah Campus,Pakistan","institution_ids":["https://openalex.org/I16076960"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087423132","display_name":"Jamal Hussain Shah","orcid":"https://orcid.org/0000-0002-7903-9391","observed_orcids":["https://orcid.org/0000-0002-7903-9391"]},"institutions":[{"id":"https://openalex.org/I16076960","display_name":"COMSATS University Islamabad","ror":"https://ror.org/00nqqvk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I16076960"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Jamal Hussain Shah","raw_affiliation_strings":["COMSATS University Islamabad,Department of Computer Sciences,Wah Campus,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"COMSATS University Islamabad,Department of Computer Sciences,Wah Campus,Pakistan","institution_ids":["https://openalex.org/I16076960"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101501542","display_name":"Muhammad Sharif","orcid":"https://orcid.org/0000-0002-7258-8400","observed_orcids":["https://orcid.org/0000-0002-7258-8400","https://orcid.org/0009-0005-4047-2787"]},"institutions":[{"id":"https://openalex.org/I16076960","display_name":"COMSATS University Islamabad","ror":"https://ror.org/00nqqvk19","country_code":"PK","type":"education","lineage":["https://openalex.org/I16076960"]}],"countries":["PK"],"is_corresponding":false,"raw_author_name":"Muhammad Sharif","raw_affiliation_strings":["COMSATS University Islamabad,Department of Computer Sciences,Wah Campus,Pakistan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"COMSATS University Islamabad,Department of Computer Sciences,Wah Campus,Pakistan","institution_ids":["https://openalex.org/I16076960"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I16076960"],"apc_list":null,"apc_paid":null,"fwci":0.2311,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.51220104,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"108","last_page":"113"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.989300012588501,"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":0.989300012588501,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.002899999963119626,"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":0.0020000000949949026,"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/left-ventricle-segmentation","display_name":"left ventricle segmentation","score":0.921999990940094},{"id":"https://openalex.org/keywords/cardiac-mri","display_name":"cardiac MRI","score":0.9520000219345093},{"id":"https://openalex.org/keywords/faster-r-cnn","display_name":"Faster R-CNN","score":0.9959999918937683},{"id":"https://openalex.org/keywords/inception-resnet","display_name":"Inception-ResNet","score":0.9900000095367432},{"id":"https://openalex.org/keywords/roi-extraction","display_name":"region of interest extraction","score":0.7919999957084656},{"id":"https://openalex.org/keywords/limited-training-data","display_name":"limited training data","score":0.9290000200271606},{"id":"https://openalex.org/keywords/overfitting","display_name":"overfitting","score":0.8880000114440918},{"id":"https://openalex.org/keywords/cardiac-segmentation","display_name":"cardiac image segmentation","score":0.31299999356269836}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8222675323486328},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.746316134929657},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7402712106704712},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5860933065414429},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5785179138183594},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5345375537872314},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5193001627922058},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.49624210596084595},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4814719259738922},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4452105760574341},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43801599740982056},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4350525736808777},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4187834858894348},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2747713625431061},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.14804622530937195}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/fit53504.2021.00029","doi":"https://doi.org/10.1109/fit53504.2021.00029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/fit53504.2021.00029","pdf_url":null,"source":{"id":"https://openalex.org/S7407087284","display_name":"International Conference on Frontiers of Information Technology (FIT)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Conference on Frontiers of Information Technology (FIT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6240000128746033,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1530452555","https://openalex.org/W1817071290","https://openalex.org/W1977772244","https://openalex.org/W1987512289","https://openalex.org/W2035933013","https://openalex.org/W2036868818","https://openalex.org/W2038952578","https://openalex.org/W2045784023","https://openalex.org/W2065768018","https://openalex.org/W2076426701","https://openalex.org/W2090873685","https://openalex.org/W2103005517","https://openalex.org/W2109178950","https://openalex.org/W2109791802","https://openalex.org/W2124632179","https://openalex.org/W2135619284","https://openalex.org/W2144885778","https://openalex.org/W2146308017","https://openalex.org/W2156437051","https://openalex.org/W2181777778","https://openalex.org/W2351103387","https://openalex.org/W2593228870","https://openalex.org/W2594577197","https://openalex.org/W2602088477","https://openalex.org/W2762541033","https://openalex.org/W2764063958","https://openalex.org/W2797946163","https://openalex.org/W2809100241","https://openalex.org/W2886590014","https://openalex.org/W2952068604","https://openalex.org/W2954584728","https://openalex.org/W2963350184","https://openalex.org/W2968435827","https://openalex.org/W2969791356","https://openalex.org/W2971541566","https://openalex.org/W2988970523","https://openalex.org/W3018794639","https://openalex.org/W3044817893","https://openalex.org/W3111662681","https://openalex.org/W3127181174","https://openalex.org/W3176727534","https://openalex.org/W3183245287","https://openalex.org/W3196752410","https://openalex.org/W3209062824","https://openalex.org/W4205182665","https://openalex.org/W6734698679","https://openalex.org/W6745067580"],"related_works":["https://openalex.org/W4296990339","https://openalex.org/W2295802084","https://openalex.org/W3002690440","https://openalex.org/W2337438617","https://openalex.org/W3011573465","https://openalex.org/W2792115253","https://openalex.org/W7140185667","https://openalex.org/W2783873016","https://openalex.org/W2914545845","https://openalex.org/W3097421522"],"abstract_inverted_index":{"Convolution":[0],"neural":[1,55,106,173],"networks":[2,56,107,114],"are":[3,20,35,74,115,150,208],"good":[4],"choice":[5],"for":[6,177,197],"localization":[7,179],"and":[8,23,43,84,140,180,214,218],"extracting":[9],"ROI":[10],"(Region":[11],"of":[12,45,50,104,120],"Interest)":[13],"in":[14,90,130,137,160],"MR":[15],"images.":[16],"But":[17],"such":[18],"algorithms":[19,73],"data":[21],"hungry":[22],"need":[24],"huge":[25],"training":[26,189],"dataset.":[27],"Unfortunately,":[28],"the":[29,77,91,118,201],"publically":[30,202],"available":[31,203],"LV":[32,178],"segmentation":[33,181],"datasets":[34,66,207],"not":[36],"large":[37,88],"enough":[38],"to":[39,58,62,69,95,134],"cover":[40],"all":[41],"variations":[42],"dynamics":[44],"heart":[46,78],"chambers.":[47],"The":[48,127],"accuracy":[49,159],"traditionally":[51],"employed":[52,176],"very":[53],"deep":[54,105,172],"tend":[57],"get":[59,157],"reduce":[60],"due":[61,68],"over-fitting":[63],"on":[64],"small":[65],"and,":[67],"this":[70,97],"factor,":[71],"these":[72],"inefficient":[75],"whenever":[76],"shape":[79],"is":[80,86,195],"outside":[81],"learning":[82],"dataset":[83],"there":[85],"a":[87,101],"bias":[89],"segmentation.":[92],"We":[93],"tried":[94],"solve":[96],"problem":[98,199],"by":[99],"using":[100],"special":[102],"kind":[103],"called":[108],"faster":[109,169],"R-CNN":[110,167,185],"Inception-ResNet":[111],"networks.":[112],"Such":[113],"equipped":[116],"with":[117,146,191,223],"power":[119],"inception":[121,128],"as":[122,124,200],"well":[123],"residual":[125,147],"modules.":[126],"modules,":[129],"our":[131,166,184,198],"architecture,":[132],"help":[133],"learn":[135],"information":[136],"image":[138,206],"locally":[139],"globally":[141],"whereas":[142],"it":[143],"also":[144],"empowered":[145],"modules":[148],"which":[149,194],"computationally":[151],"less":[152],"expensive":[153],"because":[154],"we":[155],"can":[156,187],"higher":[158],"lower":[161],"epochs.":[162],"This":[163],"fact":[164],"makes":[165],"architecture":[168,186],"than":[170],"traditional":[171],"network":[174],"architectures":[175],"task.":[182],"Also":[183],"extract":[188],"features":[190],"multiple":[192],"abstractions":[193],"suitable":[196],"cardiac":[204],"MRI":[205],"small,":[209],"captured":[210],"through":[211],"different":[212],"viewpoints":[213],"have":[215],"inhomogeneous":[216],"spatial":[217],"intensity":[219],"level":[220],"resolution":[221],"along":[222],"inherent":[224],"noise.":[225]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":3}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2022-02-13T00:00:00"}