{"id":"https://openalex.org/W3010431109","doi":"https://doi.org/10.3390/s20051392","title":"Semantically Guided Large Deformation Estimation with Deep Networks","display_name":"Semantically Guided Large Deformation Estimation with Deep Networks","publication_year":2020,"publication_date":"2020-03-04","ids":{"openalex":"https://openalex.org/W3010431109","doi":"https://doi.org/10.3390/s20051392","mag":"3010431109","pmid":"https://pubmed.ncbi.nlm.nih.gov/32143297"},"language":"en","primary_location":{"id":"doi:10.3390/s20051392","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20051392","pdf_url":"https://www.mdpi.com/1424-8220/20/5/1392/pdf?version=1583317309","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/1424-8220/20/5/1392/pdf?version=1583317309","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5011800196","display_name":"In Young Ha","orcid":"https://orcid.org/0000-0003-1904-2270"},"institutions":[{"id":"https://openalex.org/I9341345","display_name":"University of L\u00fcbeck","ror":"https://ror.org/00t3r8h32","country_code":"DE","type":"education","lineage":["https://openalex.org/I9341345"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"In Young Ha","raw_affiliation_strings":["Institute of medical informatics, University of Luebeck, 23558 Luebeck, Germany"],"raw_orcid":"https://orcid.org/0000-0003-1904-2270","affiliations":[{"raw_affiliation_string":"Institute of medical informatics, University of Luebeck, 23558 Luebeck, Germany","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051753610","display_name":"Matthias Wilms","orcid":"https://orcid.org/0000-0001-8845-360X"},"institutions":[{"id":"https://openalex.org/I168635309","display_name":"University of Calgary","ror":"https://ror.org/03yjb2x39","country_code":"CA","type":"education","lineage":["https://openalex.org/I168635309"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Matthias Wilms","raw_affiliation_strings":["Department of Radiology, University of Calgary, Calgary, AB T2N 4N1, Canada"],"raw_orcid":"https://orcid.org/0000-0001-8845-360X","affiliations":[{"raw_affiliation_string":"Department of Radiology, University of Calgary, Calgary, AB T2N 4N1, Canada","institution_ids":["https://openalex.org/I168635309"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064304390","display_name":"Mattias P. Heinrich\u202c","orcid":"https://orcid.org/0000-0002-7489-1972"},"institutions":[{"id":"https://openalex.org/I9341345","display_name":"University of L\u00fcbeck","ror":"https://ror.org/00t3r8h32","country_code":"DE","type":"education","lineage":["https://openalex.org/I9341345"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mattias Heinrich","raw_affiliation_strings":["Institute of medical informatics, University of Luebeck, 23558 Luebeck, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of medical informatics, University of Luebeck, 23558 Luebeck, Germany","institution_ids":["https://openalex.org/I9341345"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5011800196"],"corresponding_institution_ids":["https://openalex.org/I9341345"],"apc_list":{"value":2400,"currency":"CHF","value_usd":2673},"apc_paid":{"value":2400,"currency":"CHF","value_usd":2673},"fwci":1.1562,"has_fulltext":true,"cited_by_count":19,"citation_normalized_percentile":{"value":0.7785645,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"20","issue":"5","first_page":"1392","last_page":"1392"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994000196456909,"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":0.9994000196456909,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9983000159263611,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9952999949455261,"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/computer-science","display_name":"Computer science","score":0.7526381015777588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7367307543754578},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6769949197769165},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6026349067687988},{"id":"https://openalex.org/keywords/parameterized-complexity","display_name":"Parameterized complexity","score":0.5837612152099609},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5542581677436829},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.5470229387283325},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5451182723045349},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.41973739862442017},{"id":"https://openalex.org/keywords/network-architecture","display_name":"Network architecture","score":0.41731417179107666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41284266114234924},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.16850268840789795}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7526381015777588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7367307543754578},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6769949197769165},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6026349067687988},{"id":"https://openalex.org/C165464430","wikidata":"https://www.wikidata.org/wiki/Q1570441","display_name":"Parameterized complexity","level":2,"score":0.5837612152099609},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5542581677436829},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.5470229387283325},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5451182723045349},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.41973739862442017},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.41731417179107666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41284266114234924},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.16850268840789795},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C36289849","wikidata":"https://www.wikidata.org/wiki/Q34749","display_name":"Social science","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":5,"locations":[{"id":"doi:10.3390/s20051392","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20051392","pdf_url":"https://www.mdpi.com/1424-8220/20/5/1392/pdf?version=1583317309","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},{"id":"pmid:32143297","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/32143297","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors (Basel, Switzerland)","raw_type":null},{"id":"pmh:oai:doaj.org/article:2ffb54965ae6431eb5212cea52b0d0c7","is_oa":true,"landing_page_url":"https://doaj.org/article/2ffb54965ae6431eb5212cea52b0d0c7","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors, Vol 20, Iss 5, p 1392 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/1424-8220/20/5/1392/","is_oa":true,"landing_page_url":"http://dx.doi.org/10.3390/s20051392","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors","raw_type":"Text"},{"id":"pmh:oai:pubmedcentral.nih.gov:7085718","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7085718","pdf_url":null,"source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sensors (Basel)","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/s20051392","is_oa":true,"landing_page_url":"https://doi.org/10.3390/s20051392","pdf_url":"https://www.mdpi.com/1424-8220/20/5/1392/pdf?version=1583317309","source":{"id":"https://openalex.org/S101949793","display_name":"Sensors","issn_l":"1424-8220","issn":["1424-8220"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Sensors","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6031571241","display_name":null,"funder_award_id":"HE 7364/1-2","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3010431109.pdf","grobid_xml":"https://content.openalex.org/works/W3010431109.grobid-xml"},"referenced_works_count":30,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1796263212","https://openalex.org/W1901129140","https://openalex.org/W1990937109","https://openalex.org/W2077008573","https://openalex.org/W2113576511","https://openalex.org/W2134236847","https://openalex.org/W2163879248","https://openalex.org/W2299400169","https://openalex.org/W2517490240","https://openalex.org/W2560474170","https://openalex.org/W2608822622","https://openalex.org/W2751297520","https://openalex.org/W2752785527","https://openalex.org/W2753461941","https://openalex.org/W2767463438","https://openalex.org/W2787740020","https://openalex.org/W2804047627","https://openalex.org/W2891064393","https://openalex.org/W2891631795","https://openalex.org/W2949687197","https://openalex.org/W2963548592","https://openalex.org/W2963782415","https://openalex.org/W2963904328","https://openalex.org/W3098269293","https://openalex.org/W3098315580","https://openalex.org/W3104164805","https://openalex.org/W4206996524","https://openalex.org/W6618372016","https://openalex.org/W6718942301"],"related_works":["https://openalex.org/W2051058708","https://openalex.org/W1494268238","https://openalex.org/W154868527","https://openalex.org/W1983207144","https://openalex.org/W2490706771","https://openalex.org/W2480116122","https://openalex.org/W4255576661","https://openalex.org/W1516574938","https://openalex.org/W2625725254","https://openalex.org/W2563912921"],"abstract_inverted_index":{"Deformable":[0],"image":[1,191],"registration":[2,163],"is":[3,49,65],"still":[4],"a":[5,39,61,98,124,178],"challenge":[6],"when":[7],"the":[8,54,132,135,156],"considered":[9,123],"images":[10],"have":[11,121],"strong":[12,32],"variations":[13],"in":[14,29,111,145,153,171,186],"appearance":[15],"and":[16,43,92,104,142,158,173,189],"large":[17,57],"initial":[18],"misalignment.":[19],"A":[20],"huge":[21],"performance":[22],"gap":[23],"currently":[24],"remains":[25],"for":[26,53,137,177],"fast-moving":[27],"regions":[28],"videos":[30],"or":[31],"deformations":[33],"of":[34,56,79,113,134,180],"natural":[35],"objects.":[36],"We":[37,59],"present":[38],"new":[40],"semantically":[41,73],"guided":[42],"two-step":[44],"deep":[45],"deformation":[46],"network":[47,129],"that":[48,64,120],"particularly":[50],"well":[51],"suited":[52],"estimation":[55],"deformations.":[58,88],"combine":[60],"U-Net":[62],"architecture":[63],"weakly":[66],"supervised":[67],"with":[68,76,85,97],"segmentation":[69],"information":[70],"to":[71,101,117,155],"extract":[72],"meaningful":[74],"features":[75],"multiple":[77],"stages":[78],"nonrigid":[80],"spatial":[81],"transformer":[82],"networks":[83],"parameterized":[84],"low-dimensional":[86],"B-spline":[87],"Combining":[89],"alignment":[90,114,126,141,184],"loss":[91,94],"semantic":[93],"functions":[95],"together":[96],"regularization":[99],"penalty":[100],"obtain":[102],"smooth":[103],"plausible":[105],"deformations,":[106],"we":[107],"achieve":[108],"superior":[109],"results":[110],"terms":[112],"quality":[115],"compared":[116],"previous":[118],"approaches":[119],"only":[122],"label-driven":[125],"loss.":[127],"Our":[128],"model":[130],"advances":[131],"state":[133],"art":[136],"inter-subject":[138],"face":[139],"part":[140],"motion":[143],"tracking":[144,182],"medical":[146,190],"cardiac":[147],"magnetic":[148],"resonance":[149],"imaging":[150],"(MRI)":[151],"sequences":[152],"comparison":[154],"FlowNet":[157],"Label-Reg,":[159],"two":[160],"recent":[161],"deep-learning":[162],"frameworks.":[164],"The":[165],"models":[166],"are":[167],"compact,":[168],"very":[169],"fast":[170],"inference,":[172],"demonstrate":[174],"clear":[175],"potential":[176],"variety":[179],"challenging":[181],"and/or":[183],"tasks":[185],"computer":[187],"vision":[188],"analysis.":[192]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4}],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-10-10T00:00:00"}
