{"id":"https://openalex.org/W4305006393","doi":"https://doi.org/10.48550/arxiv.2210.04979","title":"Label-free segmentation from cardiac ultrasound using self-supervised learning","display_name":"Label-free segmentation from cardiac ultrasound using self-supervised learning","publication_year":2022,"publication_date":"2022-10-10","ids":{"openalex":"https://openalex.org/W4305006393","doi":"https://doi.org/10.48550/arxiv.2210.04979"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2210.04979","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.04979","pdf_url":"https://arxiv.org/pdf/2210.04979","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2210.04979","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065287401","display_name":"Danielle L. Ferreira","orcid":"https://orcid.org/0000-0003-3469-9641"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ferreira, Danielle L.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":null,"display_name":"Lau, Connor","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lau, Connor","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018454170","display_name":"Zaynaf Salaymang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Salaymang, Zaynaf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5064018474","display_name":"Rima Arnaout","orcid":"https://orcid.org/0000-0002-7134-0040"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arnaout, Rima","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5065287401"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"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/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.9970999956130981,"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"}},"topics":[{"id":"https://openalex.org/T10821","display_name":"Cardiovascular Function and Risk Factors","score":0.9970999956130981,"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/T10172","display_name":"Cardiac Valve Diseases and Treatments","score":0.9937999844551086,"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/T10372","display_name":"Cardiac Imaging and Diagnostics","score":0.9833999872207642,"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/segmentation","display_name":"Segmentation","score":0.7537295818328857},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7225245237350464},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6497678756713867},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6194807291030884},{"id":"https://openalex.org/keywords/ultrasound","display_name":"Ultrasound","score":0.5563296675682068},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4981822967529297},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4856325685977936},{"id":"https://openalex.org/keywords/gold-standard","display_name":"Gold standard (test)","score":0.4755812883377075},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.46146470308303833},{"id":"https://openalex.org/keywords/ventricle","display_name":"Ventricle","score":0.4605926275253296},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.3559412360191345},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.2906765937805176},{"id":"https://openalex.org/keywords/cardiology","display_name":"Cardiology","score":0.09822613000869751}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7537295818328857},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7225245237350464},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6497678756713867},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6194807291030884},{"id":"https://openalex.org/C143753070","wikidata":"https://www.wikidata.org/wiki/Q162564","display_name":"Ultrasound","level":2,"score":0.5563296675682068},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4981822967529297},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4856325685977936},{"id":"https://openalex.org/C40993552","wikidata":"https://www.wikidata.org/wiki/Q514654","display_name":"Gold standard (test)","level":2,"score":0.4755812883377075},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.46146470308303833},{"id":"https://openalex.org/C2778921608","wikidata":"https://www.wikidata.org/wiki/Q2002035","display_name":"Ventricle","level":2,"score":0.4605926275253296},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3559412360191345},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.2906765937805176},{"id":"https://openalex.org/C164705383","wikidata":"https://www.wikidata.org/wiki/Q10379","display_name":"Cardiology","level":1,"score":0.09822613000869751},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2210.04979","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.04979","pdf_url":"https://arxiv.org/pdf/2210.04979","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:doi:10.48550/arxiv.2210.04979","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2210.04979","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2210.04979","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2210.04979","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2210.04979","pdf_url":"https://arxiv.org/pdf/2210.04979","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2385859805","https://openalex.org/W2530972254","https://openalex.org/W2402911890","https://openalex.org/W2353214236","https://openalex.org/W2412248164","https://openalex.org/W832193342","https://openalex.org/W1987850981","https://openalex.org/W3025229005","https://openalex.org/W4375867731","https://openalex.org/W1560054494"],"abstract_inverted_index":{"Segmentation":[0],"and":[1,14,46,56,98,107,125,154,164,198],"measurement":[2],"of":[3,90,137,179],"cardiac":[4,9,143],"chambers":[5],"is":[6,12,147],"critical":[7],"in":[8,184],"ultrasound":[10],"but":[11,21,208],"laborious":[13,27],"poorly":[15],"reproducible.":[16],"Neural":[17],"networks":[18],"can":[19],"assist,":[20],"supervised":[22,110],"approaches":[23],"require":[24],"the":[25,69,91,148,167,171,185],"same":[26],"manual":[28,37,88],"annotations.":[29],"We":[30,49,75],"built":[31],"a":[32,193,206],"pipeline":[33,153,168],"for":[34,120,202],"self-supervised":[35],"(no":[36],"labels)":[38],"segmentation":[39,203],"combining":[40],"computer":[41],"vision,":[42],"clinical":[43,133,162],"domain":[44],"knowledge,":[45],"deep":[47],"learning.":[48],"trained":[50],"on":[51,58],"450":[52],"echocardiograms":[53,60,139],"(93,000":[54],"images)":[55],"tested":[57,77],"8,393":[59],"(4,476,266":[61],"images;":[62],"mean":[63],"61":[64],"years,":[65],"51%":[66],"female),":[67],"using":[68],"resulting":[70],"segmentations":[71],"to":[72,103,109,132,159],"calculate":[73],"biometrics.":[74],"also":[76],"against":[78],"external":[79],"images":[80],"from":[81,204],"an":[82,175],"additional":[83],"10,030":[84],"patients":[85],"with":[86,174],"available":[87],"tracings":[89],"left":[92,172],"ventricle.":[93],"r2":[94],"between":[95,152,161],"clinically":[96,196],"measured":[97],"pipeline-predicted":[99],"measurements":[100,115,156],"were":[101],"similar":[102,158],"reported":[104],"inter-clinician":[105],"variation":[106],"comparable":[108],"learning":[111],"across":[112],"several":[113],"different":[114],"(r2":[116],"0.56-0.84).":[117],"Average":[118],"accuracy":[119],"detecting":[121],"abnormal":[122],"chamber":[123],"size":[124],"function":[126],"was":[127,157],"0.85":[128],"(range":[129],"0.71-0.97)":[130],"compared":[131],"measurements.":[134],"A":[135],"subset":[136],"test":[138],"(n=553)":[140],"had":[141],"corresponding":[142],"MRIs,":[144],"where":[145],"MRI":[146,155],"gold":[149],"standard.":[150],"Correlation":[151],"that":[160],"echocardiogram":[163],"MRI.":[165],"Finally,":[166],"accurately":[169],"segments":[170],"ventricle":[173],"average":[176],"Dice":[177],"score":[178],"0.89":[180],"(95%":[181],"CI":[182],"[0.89])":[183],"external,":[186],"manually":[187],"labeled":[188],"dataset.":[189],"Our":[190],"results":[191],"demonstrate":[192],"manual-label":[194],"free,":[195],"valid,":[197],"highly":[199],"scalable":[200],"method":[201],"ultrasound,":[205],"noisy":[207],"globally":[209],"important":[210],"imaging":[211],"modality.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2}],"updated_date":"2026-05-16T08:24:45.110214","created_date":"2025-10-10T00:00:00"}