{"id":"https://openalex.org/W2139698799","doi":null,"title":"A Discussion on the Evaluation of A New Automatic Liver Volume Segmentation Method for Specified CT Image Datasets","display_name":"A Discussion on the Evaluation of A New Automatic Liver Volume Segmentation Method for Specified CT Image Datasets","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2139698799","mag":"2139698799"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.93.1494","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.93.1494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p167.pdf","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Ying Chi","orcid":"https://orcid.org/0000-0002-3020-7892","observed_orcids":["https://orcid.org/0000-0002-3020-7892"]},"institutions":[{"id":"https://openalex.org/I47508984","display_name":"Imperial College London","ror":"https://ror.org/041kmwe10","country_code":"GB","type":"education","lineage":["https://openalex.org/I47508984"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ying Chi","raw_affiliation_strings":["Imperial College London,#TAB#"],"raw_orcid":"https://orcid.org/0000-0002-3020-7892","affiliations":[{"raw_affiliation_string":"Imperial College London,#TAB#","institution_ids":["https://openalex.org/I47508984"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111964841","display_name":"P M Cashman","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"P M Cashman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031207765","display_name":"Fernando Bello","orcid":"https://orcid.org/0000-0003-4136-0355","observed_orcids":["https://orcid.org/0000-0003-4136-0355"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fernando Bello","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-4136-0355","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082526083","display_name":"Richard Ian Kitney","orcid":"https://orcid.org/0000-0002-6499-5209","observed_orcids":["https://orcid.org/0000-0002-6499-5209"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"R.I. Kitney","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-6499-5209","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5141,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.86417779,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9994999766349792,"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":0.9994999766349792,"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":0.00019999999494757503,"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"}},{"id":"https://openalex.org/T11984","display_name":"Anatomy and Medical Technology","score":9.999999747378752e-05,"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/ct-images","display_name":"CT images","score":0.6940000057220459},{"id":"https://openalex.org/keywords/snakes","display_name":"snakes","score":0.652999997138977},{"id":"https://openalex.org/keywords/gradient-vector-flow-snake","display_name":"gradient vector flow snake","score":0.718999981880188},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.9990000128746033},{"id":"https://openalex.org/keywords/virtual-reality","display_name":"virtual reality","score":0.5809999704360962},{"id":"https://openalex.org/keywords/anatomical-variation","display_name":"anatomical variation","score":0.7749999761581421},{"id":"https://openalex.org/keywords/edge-enhancement","display_name":"edge enhancement","score":0.5979999899864197}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7081013917922974},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6499388813972473},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6479535102844238},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5845083594322205},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.5286874175071716},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.48636892437934875},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4856318533420563},{"id":"https://openalex.org/C20749125","wikidata":"https://www.wikidata.org/wiki/Q7917826","display_name":"Vector flow","level":4,"score":0.48516184091567993},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4573364853858948},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4517698287963867},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4203140139579773},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39374464750289917},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21302494406700134},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17862585186958313},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0907922089099884},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"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},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.93.1494","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.93.1494","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p167.pdf","raw_type":"text"},{"id":"mag:2139698799","doi":null,"is_oa":false,"landing_page_url":"http://mbi.dkfz-heidelberg.de/grand-challenge2007/web/p167.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7246000170707703,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W14750675","https://openalex.org/W1604679511","https://openalex.org/W1982753412","https://openalex.org/W2009733253","https://openalex.org/W2012290245","https://openalex.org/W2104095591","https://openalex.org/W2147756293"],"related_works":["https://openalex.org/W7097468594","https://openalex.org/W1975258254","https://openalex.org/W2018761340","https://openalex.org/W2319834757","https://openalex.org/W2247983688","https://openalex.org/W3140016736","https://openalex.org/W2116532982","https://openalex.org/W2167630013","https://openalex.org/W2010234843","https://openalex.org/W2349341042"],"abstract_inverted_index":{"Abstract.":[0],"This":[1,139,190],"paper":[2,140],"presents":[3,141],"discussions":[4],"on":[5,206],"experimental":[6],"result":[7],"evaluation":[8],"outcomes":[9],"of":[10,34,54,81,117],"a":[11,142,167,183],"new":[12,143],"liver":[13,25,55,62,82,112,133,153],"volume":[14,154],"segmentation":[15,27],"method":[16,148,165,192],"developed":[17],"for":[18,149],"10":[19,208],"specified":[20,198],"CT":[21,199],"image":[22,200],"datasets.":[23,210],"Precise":[24],"surface":[26,63],"is":[28,57,137],"the":[29,35,51,61,73,79,111,115,118,125,132],"first":[30],"step":[31],"and":[32,70,83,97,114,131,151,171,202,204],"one":[33],"major":[36,48],"tasks":[37],"in":[38,72,87,108,134],"individual":[39],"surgical":[40],"resection":[41],"virtual":[42],"reality":[43],"simulations.":[44],"There":[45],"are":[46,85],"five":[47],"difficulties:":[49],"Firstly,":[50],"automatic":[52,144],"initialization":[53],"detection":[56],"often":[58],"unreliable.":[59],"Secondly,":[60],"shows":[64],"great":[65],"anatomical":[66],"variations":[67],"amongst":[68],"patients":[69],"even":[71],"same":[74],"patient":[75],"over":[76],"time;":[77],"Thirdly,":[78],"intensities":[80],"kidney":[84],"similar":[86],"certain":[88],"scanning":[89],"planes,":[90],"which":[91,156],"makes":[92],"their":[93],"contacting":[94],"boundaries":[95],"weaker":[96],"fuzzier":[98],"than":[99],"most":[100],"hepatic":[101],"vessel":[102],"or":[103],"portal":[104],"vein":[105],"walls;":[106],"Fourthly,":[107],"some":[109],"cases":[110,136],"tail":[113],"apex":[116],"heart":[119],"can":[120],"easily":[121],"be":[122],"confused.":[123],"Finally,":[124],"boundary":[126],"between":[127],"subcostal":[128],"fat":[129],"tissue":[130],"many":[135],"ambiguous.":[138],"strategic":[145],"active":[146],"contour":[147],"accurate":[150],"reproducible":[152],"segmentation,":[155],"contains":[157],"different":[158,162],"ways":[159],"to":[160],"tackle":[161],"difficulties.":[163],"Our":[164],"integrates":[166],"rotational":[168],"template":[169],"matching,":[170],"k-means":[172],"clustering":[173],"followed":[174],"by":[175,196],"rib":[176],"cage":[177],"area":[178],"local":[179],"edge":[180],"enhancement,":[181],"with":[182],"GVF":[184],"(Gradient":[185],"Vector":[186],"Flow)":[187],"geometric":[188],"Snake.":[189],"proposed":[191],"has":[193],"been":[194],"trained":[195],"20":[197],"datasets,":[201],"implemented":[203],"evaluated":[205],"another":[207],"testing":[209],"1":[211]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2017,"cited_by_count":4},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":3},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":2}],"updated_date":"2026-09-27T08:07:27.782135","created_date":"2016-06-24T00:00:00"}