{"id":"https://openalex.org/W7143465692","doi":"https://doi.org/10.48550/arxiv.2603.26188","title":"OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement","display_name":"OSA: Echocardiography Video Segmentation via Orthogonalized State Update and Anatomical Prior-aware Feature Enhancement","publication_year":2026,"publication_date":"2026-03-27","ids":{"openalex":"https://openalex.org/W7143465692","doi":"https://doi.org/10.48550/arxiv.2603.26188"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.26188","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26188","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.26188","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130966328","display_name":"Rui Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Rui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109742683","display_name":"Huisi Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Huisi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130967295","display_name":"Jing Qin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qin, Jing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.16519999504089355,"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":0.16519999504089355,"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/T12419","display_name":"Phonocardiography and Auscultation Techniques","score":0.10130000114440918,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/T11021","display_name":"ECG Monitoring and Analysis","score":0.10090000182390213,"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":0.6207000017166138},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5767999887466431},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5210999846458435},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.44510000944137573},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.40389999747276306},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.39590001106262207},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.38119998574256897},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.35260000824928284},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.35089999437332153}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.705299973487854},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6207000017166138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6021000146865845},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5767999887466431},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5210999846458435},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.44510000944137573},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41339999437332153},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.40389999747276306},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.39590001106262207},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.38119998574256897},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.35260000824928284},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.35089999437332153},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3228999972343445},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C129782007","wikidata":"https://www.wikidata.org/wiki/Q162886","display_name":"Euclidean geometry","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2702000141143799},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C120174047","wikidata":"https://www.wikidata.org/wiki/Q847073","display_name":"Euclidean distance","level":2,"score":0.257099986076355},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.2563999891281128},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2538999915122986},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.26188","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26188","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.26188","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.26188","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"and":[1,19,34,123,159,169],"temporally":[2],"consistent":[3],"segmentation":[4,167],"of":[5],"the":[6,16,63,90,94,99,107,116,147,157],"left":[7],"ventricle":[8],"from":[9,139],"echocardiography":[10],"videos":[11],"is":[12],"essential":[13],"for":[14,47,177],"estimating":[15],"ejection":[17],"fraction":[18],"assessing":[20],"cardiac":[21],"function.":[22],"However,":[23],"modeling":[24],"spatiotemporal":[25],"dynamics":[26],"remains":[27],"difficult":[28],"due":[29],"to":[30,119],"severe":[31],"speckle":[32,140],"noise":[33,141],"rapid":[35],"non-rigid":[36],"deformations.":[37],"Existing":[38],"linear":[39],"recurrent":[40],"models":[41],"offer":[42],"efficient":[43],"in-context":[44],"associative":[45],"recall":[46],"temporal":[48,126,148,170],"tracking,":[49],"but":[50],"rely":[51],"on":[52,93,115,156],"unconstrained":[53],"state":[54,64,91],"updates,":[55],"which":[56,105],"cause":[57],"progressive":[58],"singular":[59],"value":[60],"decay":[61],"in":[62,73],"matrix,":[65],"a":[66,86,143],"phenomenon":[67],"known":[68],"as":[69,110],"rank":[70,121],"collapse,":[71],"resulting":[72],"anatomical":[74,137],"details":[75],"being":[76],"overwhelmed":[77],"by":[78],"noise.":[79],"To":[80],"address":[81],"this,":[82],"we":[83],"propose":[84],"OSA,":[85],"framework":[87],"that":[88,163],"constrains":[89],"evolution":[92,109],"Stiefel":[95,117],"manifold.":[96],"We":[97],"introduce":[98],"Orthogonalized":[100],"State":[101],"Update":[102],"(OSU)":[103],"mechanism,":[104],"formulates":[106],"memory":[108],"Euclidean":[111],"projected":[112],"gradient":[113],"descent":[114],"manifold":[118],"prevent":[120],"collapse":[122],"maintain":[124],"stable":[125],"transitions.":[127],"Furthermore,":[128],"an":[129],"Anatomical":[130],"Prior-aware":[131],"Feature":[132],"Enhancement":[133],"module":[134],"explicitly":[135],"separates":[136],"structures":[138],"through":[142],"physics-driven":[144],"process,":[145],"providing":[146],"tracker":[149],"with":[150],"noise-resilient":[151],"structural":[152],"cues.":[153],"Comprehensive":[154],"experiments":[155],"CAMUS":[158],"EchoNet-Dynamic":[160],"datasets":[161],"show":[162],"OSA":[164],"achieves":[165],"state-of-the-art":[166],"accuracy":[168],"stability,":[171],"while":[172],"maintaining":[173],"real-time":[174],"inference":[175],"efficiency":[176],"clinical":[178],"deployment.":[179],"Codes":[180],"are":[181],"available":[182],"at":[183],"https://github.com/wangrui2025/OSA.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-31T00:00:00"}
