{"id":"https://openalex.org/W2032767338","doi":"https://doi.org/10.1117/12.2041690","title":"Bilayered anatomically constrained split-and-merge expectation maximisation algorithm (BiASM) for brain segmentation","display_name":"Bilayered anatomically constrained split-and-merge expectation maximisation algorithm (BiASM) for brain segmentation","publication_year":2014,"publication_date":"2014-03-21","ids":{"openalex":"https://openalex.org/W2032767338","doi":"https://doi.org/10.1117/12.2041690","mag":"2032767338"},"language":"en","primary_location":{"id":"doi:10.1117/12.2041690","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2041690","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},"type":"conference-paper","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/A5044422433","display_name":"Carole H. Sudre","orcid":"https://orcid.org/0000-0001-5753-428X"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Carole H. Sudre","raw_affiliation_strings":["Univ. College London (United Kingdom)","University College, London, United Kingdom;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. College London (United Kingdom)","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"University College, London, United Kingdom;","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077080413","display_name":"M. Jorge Cardoso","orcid":"https://orcid.org/0000-0003-1284-2558"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"M. Jorge Cardoso","raw_affiliation_strings":["Univ. College London (United Kingdom)","University College, London, United Kingdom;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. College London (United Kingdom)","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"University College, London, United Kingdom;","institution_ids":["https://openalex.org/I45129253"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082106258","display_name":"S\u00e9bastien Ourselin","orcid":"https://orcid.org/0000-0002-5694-5340"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"S\u00e9bastien Ourselin","raw_affiliation_strings":["Univ. College London (United Kingdom)","University College, London, United Kingdom;"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. College London (United Kingdom)","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"University College, London, United Kingdom;","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I45129253"],"apc_list":null,"apc_paid":null,"fwci":0.3596,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.58630843,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"9034","issue":null,"first_page":"903411","last_page":"903411"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9987000226974487,"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":0.9987000226974487,"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/T10862","display_name":"AI in cancer detection","score":0.9901999831199646,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9898999929428101,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7521189451217651},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.7323489189147949},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7009238004684448},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6264607906341553},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.5412362217903137},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5349186062812805},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5107555389404297},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5076748132705688},{"id":"https://openalex.org/keywords/statistical-model","display_name":"Statistical model","score":0.46908894181251526},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45225879549980164},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3339725136756897},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12628650665283203}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7521189451217651},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.7323489189147949},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7009238004684448},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6264607906341553},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.5412362217903137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5349186062812805},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5107555389404297},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5076748132705688},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.46908894181251526},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45225879549980164},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3339725136756897},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12628650665283203},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1117/12.2041690","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2041690","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.ucl.ac.uk.OAI2:1437587","is_oa":false,"landing_page_url":"http://discovery.ucl.ac.uk/1437587/","pdf_url":null,"source":{"id":"https://openalex.org/S4306400024","display_name":"UCL Discovery (University College London)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I45129253","host_organization_name":"University College London","host_organization_lineage":["https://openalex.org/I45129253"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"In: Ourselin, S and Styner, M, (eds.) Medical Imaging 2014: Image Processing. Society of Photo-Optical Instrumentation Engineers (SPIE): San Diego, CA, USA. (2014)","raw_type":"Proceedings paper"},{"id":"pmh:oai:kclpure.kcl.ac.uk:publications/6105efe7-8d6e-4b2a-a3ff-e1637e387161","is_oa":false,"landing_page_url":"https://kclpure.kcl.ac.uk/portal/en/publications/6105efe7-8d6e-4b2a-a3ff-e1637e387161","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Sudre , C H , Cardoso , M J & Ourselin , S 2014 , Bilayered anatomically constrained split-and-merge expectation maximisation algorithm (BiASM) for brain segmentation . in Medical Imaging 2014 : Image Processing . vol. 9034 , 903411 , Medical Imaging 2014: Image Processing , San Diego, CA , United States , 16/02/2014 . https://doi.org/10.1117/12.2041690","raw_type":"contributionToPeriodical"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1506482396","https://openalex.org/W1973290871","https://openalex.org/W1998070036","https://openalex.org/W2008101963","https://openalex.org/W2038885294","https://openalex.org/W2049633694","https://openalex.org/W2065880009","https://openalex.org/W2078604986","https://openalex.org/W2096878708","https://openalex.org/W2118292158","https://openalex.org/W2142367912","https://openalex.org/W2142405592","https://openalex.org/W2143031175","https://openalex.org/W2153236418","https://openalex.org/W2161591846","https://openalex.org/W2161671092","https://openalex.org/W4230601327","https://openalex.org/W4230920194","https://openalex.org/W4236551783","https://openalex.org/W4255113042","https://openalex.org/W4376595303","https://openalex.org/W6666965742","https://openalex.org/W6680884236","https://openalex.org/W6681026828","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W1598110435","https://openalex.org/W2548184953","https://openalex.org/W1528914981","https://openalex.org/W2002739602","https://openalex.org/W1968042686","https://openalex.org/W2897881820","https://openalex.org/W2345647014","https://openalex.org/W2201192772","https://openalex.org/W3136891595","https://openalex.org/W2545529375"],"abstract_inverted_index":{"Dealing":[0],"with":[1,118,137,162,193],"pathological":[2,175,213],"tissues":[3,214],"is":[4,27,83,110],"a":[5,65,86,104,163,194],"very":[6],"challenging":[7],"task":[8],"in":[9,36,125,202,209,218],"medical":[10],"brain":[11],"segmentation.":[12],"The":[13,91,133,166],"presence":[14],"of":[15,50,52,75,88,93,97,115,121,212,221],"pathology":[16],"can":[17],"indeed":[18],"bias":[19],"the":[20,24,37,48,53,58,94,113,116,119,122,138,147,155,159,172,182,203,210,219],"ultimate":[21],"results":[22],"when":[23,174,185],"model":[25,38,54,105,117,123,160,183],"chosen":[26],"not":[28],"appropriate":[29,95],"and":[30,34,42],"lead":[31],"to":[32,56,111,127,158,170,187],"missegmentations":[33],"errors":[35],"parameters.":[39],"Model":[40],"fit":[41,124,184],"segmentation":[43,173],"accuracy":[44],"are":[45,177],"impaired":[46],"by":[47,142],"lack":[49],"flexibility":[51,157],"used":[55],"represent":[57],"data.":[59],"In":[60],"this":[61],"work,":[62],"based":[63],"on":[64,103],"finite":[66],"Gaussian":[67,89],"mixture":[68,87],"model,":[69],"we":[70],"dynamically":[71],"introduce":[72],"extra":[73],"degrees":[74],"freedom":[76],"so":[77],"that":[78],"each":[79],"anatomical":[80,144],"tissue":[81,100,176,198],"considered":[82],"modelled":[84],"as":[85,179,181,215,217],"components.":[90],"choice":[92],"number":[96],"components":[98],"per":[99,197],"class":[101],"relies":[102],"selection":[106],"criterion.":[107],"Its":[108],"purpose":[109],"balance":[112],"complexity":[114],"quality":[120],"order":[126],"avoid":[128],"overfitting":[129],"while":[130],"allowing":[131],"flexibility.":[132],"parameters":[134],"optimisation,":[135],"constrained":[136],"additional":[139],"knowledge":[140],"brought":[141],"probabilistic":[143],"atlases,":[145],"follows":[146],"expectation":[148,190],"maximisation":[149,191],"(EM)":[150],"framework.":[151],"Split-and-merge":[152],"operations":[153],"bring":[154,206],"new":[156,207],"along":[161],"data-driven":[164],"adaptation.":[165],"proposed":[167],"methodology":[168],"appears":[169],"improve":[171],"present":[178],"well":[180,216],"compared":[186],"an":[188],"atlas-based":[189],"algorithm":[192],"unique":[195],"component":[196],"class.":[199],"These":[200],"improvements":[201],"modelling":[204,220],"might":[205],"insight":[208],"characterisation":[211],"partial":[222],"volume":[223],"effect.":[224]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}