{"id":"https://openalex.org/W4307838094","doi":"https://doi.org/10.1109/tmi.2022.3218662","title":"Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging","display_name":"Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging","publication_year":2022,"publication_date":"2022-11-04","ids":{"openalex":"https://openalex.org/W4307838094","doi":"https://doi.org/10.1109/tmi.2022.3218662","pmid":"https://pubmed.ncbi.nlm.nih.gov/36318555"},"language":"en","primary_location":{"id":"doi:10.1109/tmi.2022.3218662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3218662","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5081032783","display_name":"Jisi Tang","orcid":"https://orcid.org/0000-0002-2533-5603"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jisi Tang","raw_affiliation_strings":["State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036029284","display_name":"Qing Zhou","orcid":"https://orcid.org/0000-0002-5352-5842"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qing Zhou","raw_affiliation_strings":["State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102009530","display_name":"Yuan Huang","orcid":"https://orcid.org/0000-0002-8183-4079"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuan Huang","raw_affiliation_strings":["State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5081032783"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.4876,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.56859936,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"42","issue":"2","first_page":"519","last_page":"532"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9965999722480774,"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":0.992900013923645,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7182475328445435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6420380473136902},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5593210458755493},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5201454758644104},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.47362416982650757},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.4525175094604492},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.44675564765930176},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4369763135910034},{"id":"https://openalex.org/keywords/invariant","display_name":"Invariant (physics)","score":0.43447089195251465},{"id":"https://openalex.org/keywords/medical-imaging","display_name":"Medical imaging","score":0.4321069121360779},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4181448221206665},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2918422222137451},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.17369133234024048}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7182475328445435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6420380473136902},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5593210458755493},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5201454758644104},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.47362416982650757},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.4525175094604492},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44675564765930176},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4369763135910034},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.43447089195251465},{"id":"https://openalex.org/C31601959","wikidata":"https://www.wikidata.org/wiki/Q931309","display_name":"Medical imaging","level":2,"score":0.4321069121360779},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4181448221206665},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2918422222137451},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.17369133234024048},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C37914503","wikidata":"https://www.wikidata.org/wiki/Q156495","display_name":"Mathematical physics","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000069553","descriptor_name":"Supervised Machine Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001842","descriptor_name":"Bone and Bones","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001842","descriptor_name":"Bone and Bones","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D001842","descriptor_name":"Bone and Bones","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D014057","descriptor_name":"Tomography, X-Ray Computed","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/tmi.2022.3218662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tmi.2022.3218662","pdf_url":null,"source":{"id":"https://openalex.org/S58069681","display_name":"IEEE Transactions on Medical Imaging","issn_l":"0278-0062","issn":["0278-0062","1558-254X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Medical Imaging","raw_type":"journal-article"},{"id":"pmid:36318555","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/36318555","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":"IEEE transactions on medical imaging","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.6800000071525574}],"awards":[{"id":"https://openalex.org/G2132854122","display_name":null,"funder_award_id":"51975313","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G354661447","display_name":null,"funder_award_id":"52102476","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W181601562","https://openalex.org/W647235838","https://openalex.org/W1626653188","https://openalex.org/W1965805571","https://openalex.org/W1972485825","https://openalex.org/W1976584051","https://openalex.org/W1982465050","https://openalex.org/W1985586071","https://openalex.org/W1985907520","https://openalex.org/W2047161559","https://openalex.org/W2091791686","https://openalex.org/W2100657858","https://openalex.org/W2105106232","https://openalex.org/W2127025496","https://openalex.org/W2127033124","https://openalex.org/W2133584444","https://openalex.org/W2134834581","https://openalex.org/W2137733660","https://openalex.org/W2537228944","https://openalex.org/W2558697265","https://openalex.org/W2558748708","https://openalex.org/W2560609797","https://openalex.org/W2562388386","https://openalex.org/W2725521547","https://openalex.org/W2732387187","https://openalex.org/W2883758202","https://openalex.org/W2962934458","https://openalex.org/W2963242400","https://openalex.org/W2964213305","https://openalex.org/W2981485484","https://openalex.org/W2987431950","https://openalex.org/W3034630636","https://openalex.org/W3035226307","https://openalex.org/W3035612424","https://openalex.org/W3035725276","https://openalex.org/W3093674698","https://openalex.org/W3111487461","https://openalex.org/W3157892195","https://openalex.org/W3159926201","https://openalex.org/W4206368207","https://openalex.org/W4236667477","https://openalex.org/W6606065962","https://openalex.org/W6693095674","https://openalex.org/W6784997870"],"related_works":["https://openalex.org/W1617617605","https://openalex.org/W2016839265","https://openalex.org/W2533072256","https://openalex.org/W1590689398","https://openalex.org/W2508457823","https://openalex.org/W2518599539","https://openalex.org/W2085466496","https://openalex.org/W1979794963","https://openalex.org/W2344270078","https://openalex.org/W2026509607"],"abstract_inverted_index":{"Non-rigid":[0],"registration":[1,187],"between":[2,17],"3D":[3,214],"surfaces":[4,39],"is":[5,20,63,157,173,203],"an":[6],"important":[7],"but":[8],"notorious":[9],"problem":[10],"in":[11,84,148,188,216,248],"medical":[12],"imaging,":[13],"because":[14],"finding":[15],"correspondences":[16,32,165],"non-isometric":[18],"instances":[19],"mathematically":[21],"non-trivial.":[22],"We":[23],"propose":[24],"a":[25,35,55,67,106,178,217],"novel":[26],"self-supervised":[27,218],"method":[28,131,172,184],"to":[29,100,116,159,205,209],"learn":[30,210],"shape":[31,246],"directly":[33],"from":[34,41,47,166,213],"group":[36],"of":[37,69,75,89,125,180,235],"bone":[38],"segmented":[40],"CT":[42],"scans,":[43],"without":[44],"any":[45],"supervision":[46],"time-consuming":[48],"and":[49,72,95,120,123,142,146,162,175,191,226,244],"error-prone":[50],"manual":[51],"annotations.":[52],"Relying":[53],"on":[54,139],"Siamese":[56],"architecture,":[57],"DiffusionNet":[58],"as":[59,240],"the":[60,76,90,102,114,126,136,167],"feature":[61],"extractor":[62],"jointly":[64],"trained":[65],"with":[66],"pair":[68,179],"randomly":[70],"rotated":[71],"scaled":[73],"copies":[74],"same":[77],"shape.":[78],"The":[79,220],"learned":[80,103,221],"embeddings":[81,104,115,212],"are":[82,98,223],"aligned":[83],"spectral":[85],"domain":[86],"using":[87],"eigenfunctions":[88],"Laplace-Beltrami":[91],"Operator.":[92],"Additional":[93],"normalization":[94],"regularization":[96],"losses":[97],"incorporated":[99],"guide":[101],"towards":[105],"similar":[107],"uniform":[108],"representation":[109],"over":[110],"spectrum,":[111],"which":[112,228],"promotes":[113],"encode":[117],"multiscale":[118],"features":[119,222],"advocates":[121],"sparsity":[122],"diagonality":[124],"inferred":[127,168],"functional":[128,169],"maps.":[129],"Our":[130,171],"achieves":[132],"state-of-the-art":[133],"results":[134],"among":[135],"unsupervised":[137],"methods":[138],"several":[140],"benchmarks,":[141],"presents":[143],"greater":[144],"robustness":[145],"efficacy":[147],"registering":[149],"moderately":[150],"deformed":[151],"shapes.":[152],"A":[153],"hybrid":[154],"refinement":[155],"strategy":[156],"proposed":[158],"retrieve":[160],"smooth":[161],"close-to-conformal":[163],"point-to-point":[164],"map.":[170],"orientation":[174],"discretization-invariant.":[176],"Given":[177],"near-isometric":[181],"surfaces,":[182],"our":[183],"automatically":[185],"computes":[186],"high":[189],"accuracy,":[190],"outputs":[192],"anatomically":[193],"meaningful":[194],"correspondences.":[195],"In":[196],"this":[197],"study,":[198],"we":[199],"show":[200],"that":[201],"it":[202],"possible":[204],"use":[206],"neural":[207],"networks":[208],"general":[211],"shapes":[215],"way.":[219],"multiscale,":[224],"informative,":[225],"discriminative,":[227],"might":[229],"potentially":[230],"benefit":[231],"almost":[232],"all":[233],"types":[234],"morphology-related":[236],"downstream":[237],"tasks,":[238],"such":[239],"diagnostics,":[241],"data":[242],"screening":[243],"statistical":[245],"analysis":[247],"future.":[249]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}