{"id":"https://openalex.org/W7166814935","doi":"https://doi.org/10.48550/arxiv.2606.31198","title":"Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation","display_name":"Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation","publication_year":2026,"publication_date":"2026-06-30","ids":{"openalex":"https://openalex.org/W7166814935","doi":"https://doi.org/10.48550/arxiv.2606.31198"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.31198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31198","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.2606.31198","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138970516","display_name":"Dong Yeong Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Dong Yeong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139782712","display_name":"JunGyu Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, JunGyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113468057","display_name":"J H Choi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jaewon","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090726105","display_name":"June Young Seo","orcid":"https://orcid.org/0000-0002-9572-7573"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Seo, June Young","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078222134","display_name":"Myeongseop Kim","orcid":"https://orcid.org/0000-0001-9565-2463"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Myeongseop","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051234199","display_name":"Jinwook Choi","orcid":"https://orcid.org/0000-0002-9424-9944"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choi, Jinwook","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139723559","display_name":"Taek Min Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Taek Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139750986","display_name":"Young-Gon Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Young-Gon","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1712999939918518,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.1712999939918518,"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":0.15139999985694885,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.13040000200271606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7027999758720398},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.6322000026702881},{"id":"https://openalex.org/keywords/image-warping","display_name":"Image warping","score":0.5328999757766724},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5037999749183655},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42879998683929443},{"id":"https://openalex.org/keywords/temporal-resolution","display_name":"Temporal resolution","score":0.42570000886917114},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.39309999346733093},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.36899998784065247},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3619000017642975}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.762499988079071},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7027999758720398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6495000123977661},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.6322000026702881},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5491999983787537},{"id":"https://openalex.org/C157202957","wikidata":"https://www.wikidata.org/wiki/Q1659609","display_name":"Image warping","level":2,"score":0.5328999757766724},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5037999749183655},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42879998683929443},{"id":"https://openalex.org/C119666444","wikidata":"https://www.wikidata.org/wiki/Q5977280","display_name":"Temporal resolution","level":2,"score":0.42570000886917114},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.39309999346733093},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.36899998784065247},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3619000017642975},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.36149999499320984},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.36000001430511475},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.35749998688697815},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.34610000252723694},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C137105694","wikidata":"https://www.wikidata.org/wiki/Q3407510","display_name":"Local consistency","level":4,"score":0.33880001306533813},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C56318395","wikidata":"https://www.wikidata.org/wiki/Q215928","display_name":"Transducer","level":2,"score":0.31459999084472656},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.30489999055862427},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.302700012922287},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C2778818243","wikidata":"https://www.wikidata.org/wiki/Q899552","display_name":"Optical coherence tomography","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C2776235491","wikidata":"https://www.wikidata.org/wiki/Q9625","display_name":"Prostate","level":3,"score":0.2614000141620636}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.31198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31198","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.2606.31198","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.31198","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":[{"score":0.5922552347183228,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Real-time":[0],"video":[1,144],"segmentation":[2],"of":[3,129],"the":[4,66,72,139],"prostate":[5,67],"in":[6],"Transrectal":[7],"Ultrasound":[8],"(TRUS)":[9],"is":[10,59],"essential":[11],"for":[12,141],"image-guided":[13],"interventions.":[14],"While":[15],"conventional":[16,85],"2D":[17,49],"methods":[18],"suffer":[19],"from":[20,91,103,111,154],"inter-frame":[21],"inconsistencies":[22],"by":[23,61],"disregarding":[24],"temporal":[25,45,86,174],"context,":[26],"3D":[27],"architectures":[28],"incur":[29],"prohibitive":[30],"latency.":[31],"To":[32],"resolve":[33],"this":[34,115],"dilemma,":[35],"we":[36,95,146],"present":[37],"a":[38,48,62,97,118,155],"Temporally":[39],"Consistent":[40],"Learning":[41],"Framework":[42],"that":[43],"distills":[44],"coherence":[46,125],"into":[47],"network":[50],"during":[51],"training,":[52],"preserving":[53],"single-frame":[54],"inference":[55],"efficiency.":[56],"Our":[57],"design":[58],"driven":[60],"key":[63],"clinical":[64],"observation:":[65],"exhibits":[68],"geometric":[69,148],"stability,":[70],"whereas":[71],"surrounding":[73],"acoustic":[74],"environment":[75],"fluctuates":[76],"due":[77],"to":[78,137],"physiological":[79],"motion":[80],"and":[81,132,162,173,180],"transducer":[82],"pressure.":[83],"Because":[84],"constraints":[87],"propagate":[88],"erroneous":[89],"gradients":[90],"these":[92],"unstable":[93],"regions,":[94],"introduce":[96],"Confidence-Weighted":[98],"Temporal":[99],"Consistency":[100],"objective":[101],"derived":[102],"optical":[104],"flow":[105],"warping":[106],"residuals,":[107],"selectively":[108],"attenuating":[109],"contributions":[110],"unreliable":[112],"regions.":[113],"Complementing":[114],"pixel-wise":[116],"constraint,":[117],"Dual-scale":[119],"Prototype":[120],"Alignment":[121],"Module":[122],"enforces":[123],"semantic":[124,134],"through":[126],"contrastive":[127],"optimization":[128],"local":[130],"boundary":[131],"global":[133],"features.":[135],"Furthermore,":[136],"eliminate":[138],"need":[140],"dense":[142],"per-frame":[143],"annotations,":[145],"employ":[147],"equivariance-based":[149],"pseudo-labeling":[150],"with":[151],"knowledge":[152],"distillation":[153],"pretrained":[156],"teacher.":[157],"Extensive":[158],"experiments":[159],"on":[160],"SUN-SEG":[161],"our":[163],"newly":[164],"introduced":[165],"TRUS-V":[166],"benchmark":[167],"(2,679":[168],"frames)":[169],"demonstrate":[170],"state-of-the-art":[171],"accuracy":[172],"consistency":[175],"at":[176,184],"real-time":[177],"speed.":[178],"Code":[179],"dataset":[181],"are":[182],"available":[183],"https://github.com/DYDevelop/DTC-TRUS.":[185]},"counts_by_year":[],"updated_date":"2026-07-02T06:18:51.028212","created_date":"2026-07-02T00:00:00"}
