{"id":"https://openalex.org/W2950500943","doi":"https://doi.org/10.1109/tip.2019.2922071","title":"Pseudo-Marginal MCMC Sampling for Image Segmentation Using Nonparametric Shape Priors","display_name":"Pseudo-Marginal MCMC Sampling for Image Segmentation Using Nonparametric Shape Priors","publication_year":2019,"publication_date":"2019-06-17","ids":{"openalex":"https://openalex.org/W2950500943","doi":"https://doi.org/10.1109/tip.2019.2922071","mag":"2950500943","pmid":"https://pubmed.ncbi.nlm.nih.gov/31217112"},"language":"en","primary_location":{"id":"doi:10.1109/tip.2019.2922071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2922071","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","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/A5075176537","display_name":"Ertun\u00e7 Erdil","orcid":"https://orcid.org/0000-0001-6235-4574"},"institutions":[{"id":"https://openalex.org/I35440088","display_name":"ETH Zurich","ror":"https://ror.org/05a28rw58","country_code":"CH","type":"education","lineage":["https://openalex.org/I2799323385","https://openalex.org/I35440088"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Ertunc Erdil","raw_affiliation_strings":["Computer Vision Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland"],"raw_orcid":"https://orcid.org/0000-0001-6235-4574","affiliations":[{"raw_affiliation_string":"Computer Vision Laboratory, ETH Z\u00fcrich, Z\u00fcrich, Switzerland","institution_ids":["https://openalex.org/I35440088"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015003622","display_name":"Sinan Y\u0131ld\u0131r\u0131m","orcid":"https://orcid.org/0000-0001-7980-8990"},"institutions":[{"id":"https://openalex.org/I134235054","display_name":"Sabanc\u0131 \u00dcniversitesi","ror":"https://ror.org/049asqa32","country_code":"TR","type":"education","lineage":["https://openalex.org/I134235054"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Sinan Yildirim","raw_affiliation_strings":["Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey"],"raw_orcid":"https://orcid.org/0000-0001-7980-8990","affiliations":[{"raw_affiliation_string":"Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey","institution_ids":["https://openalex.org/I134235054"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059125158","display_name":"Tolga Ta\u015fdizen","orcid":"https://orcid.org/0000-0001-6574-0366"},"institutions":[{"id":"https://openalex.org/I223532165","display_name":"University of Utah","ror":"https://ror.org/03r0ha626","country_code":"US","type":"education","lineage":["https://openalex.org/I223532165"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tolga Tasdizen","raw_affiliation_strings":["Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT, USA"],"raw_orcid":"https://orcid.org/0000-0001-6574-0366","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT, USA","institution_ids":["https://openalex.org/I223532165"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017228370","display_name":"M\u00fcjdat \u00c7etin","orcid":"https://orcid.org/0000-0002-9824-1229"},"institutions":[{"id":"https://openalex.org/I5388228","display_name":"University of Rochester","ror":"https://ror.org/022kthw22","country_code":"US","type":"education","lineage":["https://openalex.org/I5388228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mujdat Cetin","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA"],"raw_orcid":"https://orcid.org/0000-0002-9824-1229","affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, USA","institution_ids":["https://openalex.org/I5388228"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6948,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.74230257,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"28","issue":"11","first_page":"5702","last_page":"5715"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9991999864578247,"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":0.9991999864578247,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9638000130653381,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.955299973487854,"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/prior-probability","display_name":"Prior probability","score":0.7636102437973022},{"id":"https://openalex.org/keywords/posterior-probability","display_name":"Posterior probability","score":0.6306509375572205},{"id":"https://openalex.org/keywords/markov-chain-monte-carlo","display_name":"Markov chain Monte Carlo","score":0.6036860942840576},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5300778150558472},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5070891380310059},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48587507009506226},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4831070899963379},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.47023317217826843},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4695102870464325},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.46270444989204407},{"id":"https://openalex.org/keywords/density-estimation","display_name":"Density estimation","score":0.43938976526260376},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4138641357421875},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.17761951684951782}],"concepts":[{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.7636102437973022},{"id":"https://openalex.org/C57830394","wikidata":"https://www.wikidata.org/wiki/Q278079","display_name":"Posterior probability","level":3,"score":0.6306509375572205},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.6036860942840576},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5300778150558472},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5070891380310059},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48587507009506226},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4831070899963379},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47023317217826843},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4695102870464325},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.46270444989204407},{"id":"https://openalex.org/C189508267","wikidata":"https://www.wikidata.org/wiki/Q17088227","display_name":"Density estimation","level":3,"score":0.43938976526260376},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4138641357421875},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.17761951684951782},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.0},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/tip.2019.2922071","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tip.2019.2922071","pdf_url":null,"source":{"id":"https://openalex.org/S4210173141","display_name":"IEEE Transactions on Image Processing","issn_l":"1057-7149","issn":["1057-7149","1941-0042"],"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 Image Processing","raw_type":"journal-article"},{"id":"pmid:31217112","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/31217112","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 image processing : a publication of the IEEE Signal Processing Society","raw_type":null},{"id":"pmh:oai:research.sabanciuniv.edu:37234","is_oa":false,"landing_page_url":"http://research.sabanciuniv.edu/37234/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402254","display_name":"Sabanci University","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I134235054","host_organization_name":"Sabanc\u0131 \u00dcniversitesi","host_organization_lineage":["https://openalex.org/I134235054"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Article"},{"id":"pmh:oai:research.sabanciuniv.edu:46652","is_oa":false,"landing_page_url":"https://research.sabanciuniv.edu/id/eprint/46652/","pdf_url":null,"source":{"id":"https://openalex.org/S4306402254","display_name":"Sabanci University","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I134235054","host_organization_name":"Sabanc\u0131 \u00dcniversitesi","host_organization_lineage":["https://openalex.org/I134235054"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G684183875","display_name":"\u0130ki-Foton Mikroskopi G\u00f6r\u00fcnt\u00fclerinde Dendrit Dikenlerinin Otomatik Olarak B\u00f6l\u00fctlendirilmesi, S\u0131n\u0131\ufb02and\u0131r\u0131lmas\u0131 ve Takibi i\u00e7in Olas\u0131l\u0131k ve Makine \u00d6\u011frenmesi Temelli Y\u00f6ntemler","funder_award_id":"113E603","funder_id":"https://openalex.org/F4320322626","funder_display_name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu"}],"funders":[{"id":"https://openalex.org/F4320322626","display_name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","ror":"https://ror.org/04w9kkr77"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":52,"referenced_works":["https://openalex.org/W91348777","https://openalex.org/W189596042","https://openalex.org/W1653969014","https://openalex.org/W1981631581","https://openalex.org/W1986844695","https://openalex.org/W1987869189","https://openalex.org/W1992192543","https://openalex.org/W1996902942","https://openalex.org/W1998543880","https://openalex.org/W2020999234","https://openalex.org/W2028024966","https://openalex.org/W2038952578","https://openalex.org/W2042204212","https://openalex.org/W2056760934","https://openalex.org/W2071254771","https://openalex.org/W2072863821","https://openalex.org/W2075505763","https://openalex.org/W2077837045","https://openalex.org/W2079690285","https://openalex.org/W2082541576","https://openalex.org/W2091860746","https://openalex.org/W2102512156","https://openalex.org/W2104095591","https://openalex.org/W2112796928","https://openalex.org/W2113472920","https://openalex.org/W2116040950","https://openalex.org/W2124683806","https://openalex.org/W2129905273","https://openalex.org/W2141075465","https://openalex.org/W2144857435","https://openalex.org/W2147484997","https://openalex.org/W2153810084","https://openalex.org/W2155343350","https://openalex.org/W2159539239","https://openalex.org/W2160140167","https://openalex.org/W2161194249","https://openalex.org/W2257644880","https://openalex.org/W2473495292","https://openalex.org/W2495797471","https://openalex.org/W2535901150","https://openalex.org/W2595202532","https://openalex.org/W2612437719","https://openalex.org/W2736311557","https://openalex.org/W2739394833","https://openalex.org/W2889638787","https://openalex.org/W3100622648","https://openalex.org/W4233014035","https://openalex.org/W4254510799","https://openalex.org/W4289560237","https://openalex.org/W6607775107","https://openalex.org/W6728833040","https://openalex.org/W6755038652"],"related_works":["https://openalex.org/W2897195263","https://openalex.org/W2162457363","https://openalex.org/W3204476393","https://openalex.org/W2594865736","https://openalex.org/W2143813385","https://openalex.org/W1967444756","https://openalex.org/W2435841203","https://openalex.org/W2344532017","https://openalex.org/W2044374224","https://openalex.org/W2950500943"],"abstract_inverted_index":{"Segmenting":[0],"images":[1],"of":[2,35,46,103,106,117,141,146,149,158,200,225,229,266,280,304],"low":[3],"quality":[4],"or":[5],"with":[6,241],"missing":[7],"data":[8,125,218,308],"is":[9,87,162,166,204,255],"a":[10,38,61,71,77,91,95,101,115,131,281,302],"challenging":[11],"problem.":[12],"In":[13,172,220],"such":[14,159,232],"scenarios,":[15],"exploiting":[16],"statistical":[17,132,227],"prior":[18,33,84],"information":[19],"about":[20],"the":[21,28,43,50,66,83,104,123,127,139,143,147,150,201,207,226,230,236,259,264],"shapes":[22,36,48,148,267],"to":[23,42,90,152,187,222,238,257,272],"be":[24,153,287],"segmented":[25],"can":[26],"improve":[27],"segmentation":[29,54,251],"results":[30,300],"significantly.":[31],"Incorporating":[32],"density":[34,45,68,86,262],"into":[37],"Bayesian":[39],"framework":[40],"leads":[41],"posterior":[44,67,93,144,160,191,260],"segmenting":[47,291],"given":[49],"observed":[51,124],"data.":[52],"Most":[53],"algorithms":[55],"that":[56,109],"exploit":[57],"shape":[58,85,128,192,295],"priors":[59],"optimize":[60],"cost":[62],"function":[63],"based":[64,121],"on":[65,122,301],"and":[69,126,271,306],"find":[70],"point":[72,96],"estimate":[73,97],"(e.g.,":[74],"using":[75],"maximum":[76],"posteriori":[78],"estimation).":[79],"However,":[80],"especially":[81],"when":[82],"multimodal":[88,92,282],"leading":[89],"density,":[94,284],"does":[98,112],"not":[99],"provide":[100,114],"measure":[102],"degree":[105],"confidence":[107],"in":[108,263,290],"result,":[110],"neither":[111],"it":[113,212],"picture":[116],"other":[118],"probable":[119],"solutions":[120],"priors.":[129],"With":[130],"view,":[133],"addressing":[134],"these":[135],"issues":[136,240],"would":[137,286],"involve":[138],"problem":[140],"characterizing":[142],"distributions":[145,161,193],"objects":[151,292],"segmented.":[154],"An":[155],"analytic":[156],"computation":[157,198],"intractable;":[163],"however,":[164],"characterization":[165,224],"still":[167],"possible":[168],"through":[169,268],"their":[170],"samples.":[171],"this":[173],"paper,":[174],"we":[175],"propose":[176],"an":[177,233],"efficient":[178],"pseudo-marginal":[179],"Markov":[180],"chain":[181],"Monte":[182],"Carlo":[183],"(MCMC)":[184],"sampling":[185],"approach":[186,203,234,254],"draw":[188],"samples":[189],"from":[190,206,277,293],"for":[194,215],"image":[195],"segmentation.":[196],"The":[197],"time":[199],"proposed":[202],"independent":[205],"training":[208],"set":[209],"size.":[210],"Therefore,":[211],"scales":[213],"well":[214],"very":[216],"large":[217],"sets.":[219,309],"addition":[221],"better":[223],"structure":[228],"problem,":[231],"has":[235],"potential":[237],"address":[239],"getting":[242],"stuck":[243],"at":[244],"local":[245],"optima,":[246],"suffered":[247],"by":[248],"existing":[249],"shape-based":[250],"methods.":[252],"Our":[253],"able":[256],"characterize":[258],"probability":[261,283],"space":[265],"its":[269],"samples,":[270],"return":[273],"multiple":[274,294],"solutions,":[275],"potentially":[276],"different":[278],"modes":[279],"which":[285],"encountered,":[288],"e.g.,":[289],"classes.":[296],"We":[297],"present":[298],"promising":[299],"variety":[303],"synthetic":[305],"real":[307]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
