{"id":"https://openalex.org/W2110661555","doi":"https://doi.org/10.1109/isbi.2004.1398790","title":"Comparison of ventricular geometry for two real time 3D ultrasound machines with three dimensional level set","display_name":"Comparison of ventricular geometry for two real time 3D ultrasound machines with three dimensional level set","publication_year":2005,"publication_date":"2005-04-12","ids":{"openalex":"https://openalex.org/W2110661555","doi":"https://doi.org/10.1109/isbi.2004.1398790","mag":"2110661555"},"language":"en","primary_location":{"id":"doi:10.1109/isbi.2004.1398790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2004.1398790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)","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/A5008972669","display_name":"Elsa D. Angelini","orcid":"https://orcid.org/0000-0002-1602-300X"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"E. Angelini","raw_affiliation_strings":["Columbia University, New York, USA","Columbia University, New York, NY/USA"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Columbia University, New York, NY/USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089794335","display_name":"Ryo Otsuka","orcid":"https://orcid.org/0000-0002-3730-9867"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"R. Otsuka","raw_affiliation_strings":["Columbia University, New York, USA","Columbia University, New York, NY/USA"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Columbia University, New York, NY/USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109982638","display_name":"S. Homma","orcid":null},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S. Homma","raw_affiliation_strings":["Columbia University, New York, USA","Columbia University, New York, NY/USA"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Columbia University, New York, NY/USA","institution_ids":["https://openalex.org/I78577930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090489079","display_name":"Andrew F. Laine","orcid":"https://orcid.org/0000-0003-3797-0628"},"institutions":[{"id":"https://openalex.org/I78577930","display_name":"Columbia University","ror":"https://ror.org/00hj8s172","country_code":"US","type":"education","lineage":["https://openalex.org/I78577930"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Laine","raw_affiliation_strings":["Columbia University, New York, USA","Columbia University, New York, NY/USA"],"affiliations":[{"raw_affiliation_string":"Columbia University, New York, USA","institution_ids":["https://openalex.org/I78577930"]},{"raw_affiliation_string":"Columbia University, New York, NY/USA","institution_ids":["https://openalex.org/I78577930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008972669"],"corresponding_institution_ids":["https://openalex.org/I78577930"],"apc_list":null,"apc_paid":null,"fwci":0.8812,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.76761837,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1323","last_page":"1326"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9925000071525574,"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":0.9925000071525574,"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/T11366","display_name":"Elasticity and Material Modeling","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.9764000177383423,"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/segmentation","display_name":"Segmentation","score":0.8409829139709473},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.6227479577064514},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5544353723526001},{"id":"https://openalex.org/keywords/level-set","display_name":"Level set (data structures)","score":0.5456552505493164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5307581424713135},{"id":"https://openalex.org/keywords/3d-ultrasound","display_name":"3D ultrasound","score":0.4957760274410248},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.49044057726860046},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4792037904262543},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.477143257856369},{"id":"https://openalex.org/keywords/homogeneity","display_name":"Homogeneity (statistics)","score":0.4215783476829529},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.41557013988494873},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38572871685028076},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.12479451298713684},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.09968835115432739},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.06596776843070984}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8409829139709473},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.6227479577064514},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5544353723526001},{"id":"https://openalex.org/C153008295","wikidata":"https://www.wikidata.org/wiki/Q6535093","display_name":"Level set (data structures)","level":2,"score":0.5456552505493164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5307581424713135},{"id":"https://openalex.org/C2780170424","wikidata":"https://www.wikidata.org/wiki/Q229399","display_name":"3D ultrasound","level":3,"score":0.4957760274410248},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.49044057726860046},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4792037904262543},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.477143257856369},{"id":"https://openalex.org/C142259097","wikidata":"https://www.wikidata.org/wiki/Q5891314","display_name":"Homogeneity (statistics)","level":2,"score":0.4215783476829529},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41557013988494873},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38572871685028076},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.12479451298713684},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.09968835115432739},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.06596776843070984}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isbi.2004.1398790","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isbi.2004.1398790","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 2nd IEEE International Symposium on Biomedical Imaging: Macro to Nano (IEEE Cat No. 04EX821)","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":18,"referenced_works":["https://openalex.org/W178970226","https://openalex.org/W201690462","https://openalex.org/W1968439442","https://openalex.org/W1991113069","https://openalex.org/W2012118748","https://openalex.org/W2081820511","https://openalex.org/W2103559027","https://openalex.org/W2112713547","https://openalex.org/W2116040950","https://openalex.org/W2130094715","https://openalex.org/W2132363464","https://openalex.org/W2141334965","https://openalex.org/W2143235913","https://openalex.org/W2145310245","https://openalex.org/W2149019026","https://openalex.org/W2150134853","https://openalex.org/W2164822588","https://openalex.org/W2616454594"],"related_works":["https://openalex.org/W2373659438","https://openalex.org/W2392905701","https://openalex.org/W1879755808","https://openalex.org/W4313052709","https://openalex.org/W1998150108","https://openalex.org/W2986132491","https://openalex.org/W2149987068","https://openalex.org/W2171653019","https://openalex.org/W2003543191","https://openalex.org/W2186275670"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"new":[3],"results":[4],"for":[5,76],"segmentation":[6,15,27,70],"of":[7,49,62,68,82],"3D":[8,39],"ultrasound":[9,40,74],"using":[10],"a":[11,19],"robust":[12],"and":[13,52,59,72,79],"smooth":[14],"method":[16],"based":[17,42,56],"on":[18,43,57],"homogeneity-driven":[20],"three":[21],"dimensional":[22],"level":[23],"set":[24],"algorithm.":[25],"The":[26],"was":[28],"applied":[29],"to":[30],"echocardiographic":[31],"data":[32],"from":[33],"healthy":[34],"volunteers":[35],"acquired":[36],"with":[37],"two":[38],"machines":[41,75],"matrix-phased":[44],"array":[45],"technology.":[46],"A":[47],"comparison":[48],"ventricular":[50,77],"volumes":[51],"geometry":[53,78],"is":[54],"performed":[55],"manual":[58],"automatic":[60],"methods":[61,71],"segmentation.":[63],"Results":[64],"showed":[65],"good":[66],"agreement":[67],"the":[69,73],"quantitative":[80],"assessment":[81],"cardiac":[83],"function.":[84]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
