{"id":"https://openalex.org/W2315411846","doi":"https://doi.org/10.1117/12.2216514","title":"Automated 3D renal segmentation based on image partitioning","display_name":"Automated 3D renal segmentation based on image partitioning","publication_year":2016,"publication_date":"2016-03-21","ids":{"openalex":"https://openalex.org/W2315411846","doi":"https://doi.org/10.1117/12.2216514","mag":"2315411846"},"language":"en","primary_location":{"id":"doi:10.1117/12.2216514","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2216514","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":"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/A5047809985","display_name":"Varduhi Yeghiazaryan","orcid":"https://orcid.org/0009-0006-4175-7829"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Varduhi Yeghiazaryan","raw_affiliation_strings":["Univ. of Oxford (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oxford (United Kingdom)","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055611196","display_name":"Irina Voiculescu","orcid":"https://orcid.org/0000-0002-9104-8012"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Irina D. Voiculescu","raw_affiliation_strings":["Univ. of Oxford (United Kingdom)"],"affiliations":[{"raw_affiliation_string":"Univ. of Oxford (United Kingdom)","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5047809985"],"corresponding_institution_ids":["https://openalex.org/I40120149"],"apc_list":null,"apc_paid":null,"fwci":0.83591192,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.80792138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"9784","issue":null,"first_page":"97842E","last_page":"97842E"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9965000152587891,"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":0.9855999946594238,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/jaccard-index","display_name":"Jaccard index","score":0.7558228969573975},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7549988031387329},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6747691631317139},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6626337766647339},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.53404301404953},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.5161901116371155},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4925721287727356},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4921051859855652},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4734239876270294},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4486102759838104},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.44062134623527527},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.41979244351387024},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23247909545898438}],"concepts":[{"id":"https://openalex.org/C203519979","wikidata":"https://www.wikidata.org/wiki/Q865360","display_name":"Jaccard index","level":3,"score":0.7558228969573975},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7549988031387329},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6747691631317139},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6626337766647339},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.53404301404953},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.5161901116371155},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4925721287727356},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4921051859855652},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4734239876270294},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4486102759838104},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.44062134623527527},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.41979244351387024},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23247909545898438},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2216514","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2216514","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"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7400000095367432,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W1238092070","https://openalex.org/W1483079458","https://openalex.org/W1484144136","https://openalex.org/W1987869189","https://openalex.org/W1994601434","https://openalex.org/W1999244633","https://openalex.org/W2024762046","https://openalex.org/W2046622279","https://openalex.org/W2050622035","https://openalex.org/W2053588653","https://openalex.org/W2054113582","https://openalex.org/W2066579873","https://openalex.org/W2083517234","https://openalex.org/W2094363303","https://openalex.org/W2099505818","https://openalex.org/W2101211008","https://openalex.org/W2136711943","https://openalex.org/W2140067226","https://openalex.org/W2148241224","https://openalex.org/W2149184914","https://openalex.org/W2150134853","https://openalex.org/W2164751492","https://openalex.org/W2168463767","https://openalex.org/W2178114295","https://openalex.org/W2189134720","https://openalex.org/W2275051932","https://openalex.org/W2493022418","https://openalex.org/W4206519171","https://openalex.org/W4214568587","https://openalex.org/W4244441399","https://openalex.org/W6627976337","https://openalex.org/W6628896651","https://openalex.org/W6650403833","https://openalex.org/W6656721295","https://openalex.org/W6674970105","https://openalex.org/W6680679464","https://openalex.org/W6682172613","https://openalex.org/W6684170269","https://openalex.org/W6995944937","https://openalex.org/W7027716524","https://openalex.org/W7034681534"],"related_works":["https://openalex.org/W1994775821","https://openalex.org/W2012019886","https://openalex.org/W2091133150","https://openalex.org/W2331322489","https://openalex.org/W2945869148","https://openalex.org/W2953570019","https://openalex.org/W2611195251","https://openalex.org/W2398781203","https://openalex.org/W2009279505","https://openalex.org/W4206503171"],"abstract_inverted_index":{"Despite":[0],"several":[1],"decades":[2],"of":[3,35,102,117,124,188,199,209,240],"research":[4],"into":[5],"segmentation":[6,11,31,105],"techniques,":[7],"automated":[8,38,104],"medical":[9],"image":[10,56],"is":[12,230],"barely":[13],"usable":[14],"in":[15,192],"a":[16,36,44,54,237],"clinical":[17],"context,":[18],"and":[19,139,146,160,181,195],"still":[20],"at":[21],"vast":[22],"user":[23],"time":[24],"expense.":[25],"This":[26],"paper":[27],"illustrates":[28],"unsupervised":[29],"organ":[30],"through":[32],"the":[33,137,151,170,185,193,196,200,204,207,216,221,228],"use":[34],"novel":[37],"labelling":[39],"approximation":[40,50],"algorithm":[41],"followed":[42],"by":[43],"hypersurface":[45],"front":[46],"propagation":[47],"method.":[48],"The":[49,84,155],"stage":[51],"relies":[52],"on":[53,73],"pre-computed":[55],"partition":[57],"forest":[58],"obtained":[59,121,223],"directly":[60,72],"from":[61,169],"CT":[62,125],"scan":[63],"data.":[64],"We":[65],"have":[66,212],"implemented":[67],"all":[68],"procedures":[69],"to":[70,96,177,184],"operate":[71],"3D":[74],"volumes,":[75],"rather":[76],"than":[77],"slice-by-slice,":[78],"because":[79],"our":[80,103,233],"algorithms":[81],"are":[82,175],"dimensionality-independent.":[83],"results":[85],"picture":[86],"segmentations":[87],"which":[88],"identify":[89],"kidneys,":[90,129,162],"but":[91],"can":[92],"easily":[93],"be":[94],"extrapolated":[95],"other":[97],"body":[98],"parts.":[99],"Quantitative":[100],"analysis":[101,156],"compared":[106],"against":[107],"hand-segmented":[108],"gold":[109],"standards":[110],"indicates":[111],"an":[112],"average":[113],"Dice":[114,138],"similarity":[115,133,217],"coefficient":[116],"90%.":[118],"Results":[119],"were":[120,167],"over":[122],"volumes":[123],"data":[126],"with":[127],"9":[128],"computing":[130],"both":[131,158,179],"volume-based":[132],"measures":[134,148],"(such":[135,149],"as":[136,150],"Jaccard":[140],"coefficients,":[141],"true":[142],"positive":[143],"volume":[144,153],"fraction)":[145],"size-based":[147],"relative":[152],"difference).":[154],"considered":[157],"healthy":[159],"diseased":[161],"although":[163],"extreme":[164],"pathological":[165],"cases":[166,174],"excluded":[168],"overall":[171],"count.":[172],"Such":[173],"difficult":[176],"segment":[178],"manually":[180],"automatically":[182],"due":[183],"large":[186],"amplitude":[187],"Hounsfield":[189],"unit":[190],"distribution":[191],"scan,":[194],"wide":[197],"spread":[198],"tumorous":[201],"tissue":[202],"inside":[203],"abdomen.":[205],"In":[206],"case":[208],"kidneys":[210],"that":[211],"maintained":[213],"their":[214],"shape,":[215],"range":[218],"lies":[219],"around":[220],"values":[222],"for":[224],"inter-operator":[225],"variability.":[226],"Whilst":[227],"procedure":[229],"fully":[231],"automated,":[232],"tools":[234],"also":[235],"provide":[236],"light":[238],"level":[239],"manual":[241],"editing.":[242]},"counts_by_year":[{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
