{"id":"https://openalex.org/W2979518131","doi":"https://doi.org/10.1109/access.2019.2920957","title":"Automatic Left Ventricle Recognition, Segmentation and Tracking in Cardiac Ultrasound Image Sequences","display_name":"Automatic Left Ventricle Recognition, Segmentation and Tracking in Cardiac Ultrasound Image Sequences","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2979518131","doi":"https://doi.org/10.1109/access.2019.2920957","mag":"2979518131"},"language":"en","primary_location":{"id":"doi:10.1109/access.2019.2920957","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2920957","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2019.2920957","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042420817","display_name":"Wei\u2010Yen Hsu","orcid":"https://orcid.org/0000-0002-4599-0744"},"institutions":[{"id":"https://openalex.org/I148099254","display_name":"National Chung Cheng University","ror":"https://ror.org/0028v3876","country_code":"TW","type":"education","lineage":["https://openalex.org/I148099254"]}],"countries":["TW"],"is_corresponding":true,"raw_author_name":"Wei-Yen Hsu","raw_affiliation_strings":["Department of Information Management, National Chung Cheng University, Minhsiung, Taiwan"],"affiliations":[{"raw_affiliation_string":"Department of Information Management, National Chung Cheng University, Minhsiung, Taiwan","institution_ids":["https://openalex.org/I148099254"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5042420817"],"corresponding_institution_ids":["https://openalex.org/I148099254"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.3159,"has_fulltext":false,"cited_by_count":26,"citation_normalized_percentile":{"value":0.8468149,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"7","issue":null,"first_page":"140524","last_page":"140533"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965000152587891,"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":0.9965000152587891,"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/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.9908999800682068,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9710000157356262,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.78370201587677},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.712317943572998},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6988190412521362},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6085345149040222},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5088256001472473},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.4996335506439209},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.49703577160835266},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.461794376373291},{"id":"https://openalex.org/keywords/anisotropic-diffusion","display_name":"Anisotropic diffusion","score":0.45921751856803894},{"id":"https://openalex.org/keywords/ventricle","display_name":"Ventricle","score":0.43786364793777466},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41492757201194763},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.41132837533950806},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3351525664329529},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.11367806792259216}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.78370201587677},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.712317943572998},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6988190412521362},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6085345149040222},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5088256001472473},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.4996335506439209},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.49703577160835266},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.461794376373291},{"id":"https://openalex.org/C203504353","wikidata":"https://www.wikidata.org/wiki/Q4765461","display_name":"Anisotropic diffusion","level":3,"score":0.45921751856803894},{"id":"https://openalex.org/C2778921608","wikidata":"https://www.wikidata.org/wiki/Q2002035","display_name":"Ventricle","level":2,"score":0.43786364793777466},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41492757201194763},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.41132837533950806},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3351525664329529},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.11367806792259216},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2019.2920957","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2920957","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:78f2c6b183b44ed39b43e2e5e088c576","is_oa":true,"landing_page_url":"https://doaj.org/article/78f2c6b183b44ed39b43e2e5e088c576","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 7, Pp 140524-140533 (2019)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2019.2920957","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2019.2920957","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W97877240","https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1964791943","https://openalex.org/W1973391164","https://openalex.org/W1987869189","https://openalex.org/W2008124012","https://openalex.org/W2012771980","https://openalex.org/W2038952578","https://openalex.org/W2043455337","https://openalex.org/W2063466230","https://openalex.org/W2084783417","https://openalex.org/W2089468765","https://openalex.org/W2092439691","https://openalex.org/W2102605133","https://openalex.org/W2104095591","https://openalex.org/W2109255472","https://openalex.org/W2112691218","https://openalex.org/W2117333116","https://openalex.org/W2123402141","https://openalex.org/W2130462140","https://openalex.org/W2143238378","https://openalex.org/W2150134853","https://openalex.org/W2153810084","https://openalex.org/W2155215599","https://openalex.org/W2160754664","https://openalex.org/W2161969291","https://openalex.org/W2169624977","https://openalex.org/W2327084999","https://openalex.org/W2617665094","https://openalex.org/W4205953283","https://openalex.org/W6604095946","https://openalex.org/W6620707391","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W2366558118","https://openalex.org/W2393426979","https://openalex.org/W2159155702","https://openalex.org/W1999581389","https://openalex.org/W1859619809","https://openalex.org/W2348246732","https://openalex.org/W2944181031","https://openalex.org/W2356023255","https://openalex.org/W2130094715","https://openalex.org/W2173120285"],"abstract_inverted_index":{"In":[0,57,76],"this":[1,58,149],"study,":[2,59],"we":[3,60],"propose":[4,61],"a":[5],"novel":[6,109],"method":[7,110,146,169],"incorporating":[8],"faster":[9,114],"region-based":[10,115],"convolutional":[11,116],"neural":[12,117],"network":[13,118],"and":[14,22,38,50,72,82,130,157,180],"active":[15,121],"shape":[16,81,122],"model":[17,123],"to":[18,68,126,154],"automatically":[19,96,127],"recognize,":[20,128],"segment,":[21,129],"track":[23,131,158],"the":[24,45,80,85,98,101,113,120,132,145,159,171],"left":[25,86,102,133,160],"ventricle":[26,87,103,134,161],"in":[27,135,148,162,175],"cardiac":[28,136,163],"ultrasound":[29,137,164],"image":[30,48,74,138,165],"sequences,":[31],"respectively.":[32],"Ultrasound":[33],"images":[34,53],"typically":[35],"contain":[36],"noise":[37,71],"artifacts.":[39],"The":[40,167],"conventional":[41,93],"filters":[42],"cannot":[43,95],"preserve":[44],"edges":[46],"of":[47,79,84,100,177],"contours,":[49],"thus":[51],"blurry":[52],"are":[54],"often":[55],"obtained.":[56],"an":[62],"improved":[63],"adaptive":[64],"anisotropic":[65],"diffusion":[66],"filter":[67],"effectively":[69],"reduce":[70],"reinforce":[73],"contours.":[75],"addition,":[77],"because":[78],"appearance":[83],"vary":[88],"considerably":[89],"between":[90],"adjacent":[91],"images,":[92],"methods":[94],"identify":[97],"position":[99],"or":[104],"accurately":[105,155],"segment":[106,156],"them.":[107],"A":[108],"that":[111],"combines":[112],"with":[119,141],"is":[124],"proposed":[125,147,168],"sequences.":[139,166],"Compared":[140],"four":[142,185],"state-of-the-art":[143],"approaches,":[144],"study":[150],"can":[151],"be":[152],"applied":[153],"produces":[170],"most":[172],"satisfactory":[173],"results":[174],"terms":[176],"visual":[178],"presentation":[179],"segmentation":[181],"quality":[182],"based":[183],"on":[184],"criteria.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}