{"id":"https://openalex.org/W2794396789","doi":"https://doi.org/10.1117/12.2293588","title":"A multilevel Markov Chain Monte Carlo approach for uncertainty quantification in deformable registration","display_name":"A multilevel Markov Chain Monte Carlo approach for uncertainty quantification in deformable registration","publication_year":2018,"publication_date":"2018-03-02","ids":{"openalex":"https://openalex.org/W2794396789","doi":"https://doi.org/10.1117/12.2293588","mag":"2794396789"},"language":"en","primary_location":{"id":"doi:10.1117/12.2293588","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"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/A5067774453","display_name":"Sandra Schultz","orcid":null},"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":"Sandra Schultz","raw_affiliation_strings":["Univ. of L\u00fcbeck (Germany)"],"affiliations":[{"raw_affiliation_string":"Univ. of L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013972125","display_name":"Heinz Handels","orcid":"https://orcid.org/0000-0002-3499-4328"},"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":"Heinz Handels","raw_affiliation_strings":["Univ. of L\u00fcbeck (Germany)"],"affiliations":[{"raw_affiliation_string":"Univ. of L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5050326500","display_name":"Jan Ehrhardt","orcid":null},"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":"Jan Ehrhardt","raw_affiliation_strings":["Univ. of L\u00fcbeck (Germany)"],"affiliations":[{"raw_affiliation_string":"Univ. of L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5067774453"],"corresponding_institution_ids":["https://openalex.org/I9341345"],"apc_list":null,"apc_paid":null,"fwci":0.7312,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.7586843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"23","last_page":"23"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9984999895095825,"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":0.9984999895095825,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9939000010490417,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.8417987823486328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.649013102054596},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.5480674505233765},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.5102880001068115},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.5076295137405396},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.4881320893764496},{"id":"https://openalex.org/keywords/spline","display_name":"Spline (mechanical)","score":0.483437180519104},{"id":"https://openalex.org/keywords/image-registration","display_name":"Image registration","score":0.4819605052471161},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.45121458172798157},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4363018572330475},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3862982988357544},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3601696491241455},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2800315022468567},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2447989583015442},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20422780513763428},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.15890026092529297}],"concepts":[{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.8417987823486328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.649013102054596},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5480674505233765},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.5102880001068115},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5076295137405396},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.4881320893764496},{"id":"https://openalex.org/C10390562","wikidata":"https://www.wikidata.org/wiki/Q581809","display_name":"Spline (mechanical)","level":2,"score":0.483437180519104},{"id":"https://openalex.org/C166704113","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image registration","level":3,"score":0.4819605052471161},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45121458172798157},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4363018572330475},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3862982988357544},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3601696491241455},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2800315022468567},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2447989583015442},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20422780513763428},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.15890026092529297},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2293588","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2293588","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W1963049518","https://openalex.org/W1963946782","https://openalex.org/W2004477950","https://openalex.org/W2031896757","https://openalex.org/W2057623975","https://openalex.org/W2082030398","https://openalex.org/W2087322538","https://openalex.org/W2096104495","https://openalex.org/W2099602289","https://openalex.org/W2113576511","https://openalex.org/W2115167851","https://openalex.org/W2121014637","https://openalex.org/W2122448973","https://openalex.org/W2163715525","https://openalex.org/W2522083379","https://openalex.org/W2548353158","https://openalex.org/W4245795438"],"related_works":["https://openalex.org/W71678127","https://openalex.org/W2157655363","https://openalex.org/W4205763938","https://openalex.org/W2292189132","https://openalex.org/W4288092343","https://openalex.org/W2134332527","https://openalex.org/W4386114318","https://openalex.org/W2888496681","https://openalex.org/W2790979771","https://openalex.org/W2963009826"],"abstract_inverted_index":{"Image":[0],"guided":[1],"diagnostics":[2],"and":[3,9,176],"therapy":[4],"comprise":[5],"decisions":[6],"concerning":[7],"treatment":[8],"intervention":[10],"based":[11,45],"on":[12,46,183],"registered":[13],"image":[14,128],"data":[15],"of":[16,27,54,62,87,102,108,137,163,172],"patients":[17],"in":[18],"many":[19],"clinical":[20],"settings.":[21],"Therefore,":[22],"knowledge":[23],"about":[24],"the":[25,28,42,51,57,106,109,122,135,146,150,164,168,173,178],"reliability":[26,179],"registration":[29,43],"result":[30],"is":[31,105],"crucial.":[32],"In":[33,130],"this":[34,38,103,131],"paper,":[35],"we":[36,133],"tackle":[37],"issue":[39],"by":[40,71,158],"estimating":[41],"uncertainty":[44,181],"Bayesian":[47],"analysis,":[48],"i.e.":[49,145],"examining":[50],"posterior":[52,63,88,165],"distribution":[53],"parameters":[55,151],"describing":[56],"underlying":[58],"transformations.":[59],"The":[60,100],"intractability":[61],"distributions":[64],"allows":[65],"only":[66],"an":[67],"approximation,":[68],"usually":[69,125],"realized":[70],"Monte":[72,79],"Carlo":[73,80],"sampling":[74],"methods.":[75],"Conventional":[76],"Markov":[77],"Chain":[78],"(MCMC)":[81],"algorithms":[82],"require":[83],"a":[84,96,113,141,161],"large":[85],"number":[86],"samples":[89],"to":[90,152],"ensure":[91],"robust":[92],"estimates,":[93],"which":[94],"inflicts":[95],"high":[97],"computational":[98],"burden.":[99],"contribution":[101],"work":[104,132],"embedding":[107],"MCMC":[110,119],"approach":[111],"into":[112,121],"cost":[114,174],"reducing":[115],"multilevel":[116],"framework.":[117],"Multilevel":[118],"fits":[120],"multi-resolution":[123],"framework":[124],"applied":[126],"for":[127],"registration.":[129],"evaluate":[134,167],"performance":[136],"our":[138],"method":[139],"using":[140],"B-spline":[142,147],"transformation":[143],"framework,":[144],"coefficients":[148],"are":[149],"estimate.":[153],"We":[154],"demonstrate":[155],"its":[156],"correctness":[157],"comparison":[159],"with":[160],"ground-truth":[162],"distribution,":[166],"efficiency":[169],"through":[170],"examination":[171],"reduction":[175],"show":[177],"as":[180],"estimator":[182],"brain":[184],"MRI":[185],"images.":[186]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}