{"id":"https://openalex.org/W2305104982","doi":"https://doi.org/10.1117/12.2216845","title":"Random forest classification of large volume structures for visuo-haptic rendering in CT images","display_name":"Random forest classification of large volume structures for visuo-haptic rendering in CT images","publication_year":2016,"publication_date":"2016-03-21","ids":{"openalex":"https://openalex.org/W2305104982","doi":"https://doi.org/10.1117/12.2216845","mag":"2305104982"},"language":"en","primary_location":{"id":"doi:10.1117/12.2216845","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2216845","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/A5034782328","display_name":"Andr\u00e9 Mastmeyer","orcid":"https://orcid.org/0000-0003-0561-8363"},"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":"Andre Mastmeyer","raw_affiliation_strings":["Univ. zu L\u00fcbeck (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. zu L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056271657","display_name":"Dirk Fortmeier","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":"Dirk Fortmeier","raw_affiliation_strings":["Univ. zu L\u00fcbeck (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. zu L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"last","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. zu L\u00fcbeck (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. zu L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9341345"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"9784","issue":null,"first_page":"97842H","last_page":"97842H"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9951000213623047,"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":0.9951000213623047,"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":0.9925000071525574,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9830999970436096,"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/random-forest","display_name":"Random forest","score":0.8278860449790955},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7242578268051147},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6895908117294312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6741131544113159},{"id":"https://openalex.org/keywords/voxel","display_name":"Voxel","score":0.6638522148132324},{"id":"https://openalex.org/keywords/volume-rendering","display_name":"Volume rendering","score":0.5911747813224792},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5433777570724487},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.48985597491264343},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4688739776611328},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44470465183258057},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4334959089756012},{"id":"https://openalex.org/keywords/volume","display_name":"Volume (thermodynamics)","score":0.4320928752422333},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.4177873134613037},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.39601024985313416},{"id":"https://openalex.org/keywords/rendering","display_name":"Rendering (computer graphics)","score":0.34999972581863403}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.8278860449790955},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7242578268051147},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6895908117294312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6741131544113159},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6638522148132324},{"id":"https://openalex.org/C30769735","wikidata":"https://www.wikidata.org/wiki/Q2165951","display_name":"Volume rendering","level":3,"score":0.5911747813224792},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5433777570724487},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.48985597491264343},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4688739776611328},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44470465183258057},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4334959089756012},{"id":"https://openalex.org/C20556612","wikidata":"https://www.wikidata.org/wiki/Q4469374","display_name":"Volume (thermodynamics)","level":2,"score":0.4320928752422333},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.4177873134613037},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.39601024985313416},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.34999972581863403},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2216845","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2216845","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":0.7699999809265137,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1555106964","https://openalex.org/W1986303814","https://openalex.org/W2061688866","https://openalex.org/W2096579040","https://openalex.org/W2101234009","https://openalex.org/W2154158661","https://openalex.org/W2164741953","https://openalex.org/W2202955336","https://openalex.org/W2911964244","https://openalex.org/W3215186461","https://openalex.org/W6646915234","https://openalex.org/W6675354045"],"related_works":["https://openalex.org/W2397749199","https://openalex.org/W2073105412","https://openalex.org/W1545173001","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W18768452","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W2767589843","https://openalex.org/W4313289487"],"abstract_inverted_index":{"For":[0,134,157],"patient-specific":[1],"voxel-based":[2],"visuo-haptic":[3,135],"rendering":[4],"of":[5,8,16,57,66,91,109,121,163,176,183,192],"CT":[6],"scans":[7],"the":[9,12,64,67,80,88,92,105,131,166,177,215],"liver":[10],"area,":[11],"fully":[13],"automatic":[14],"segmentation":[15],"large":[17],"volume":[18,83,139],"structures":[19,140,154,208],"such":[20],"as":[21],"skin,":[22,199],"soft":[23,200],"tissue,":[24,201],"lungs":[25],"and":[26,53,63,115,196,204,221],"intestine":[27],"(risk":[28],"structures)":[29],"is":[30,60],"important.":[31],"Using":[32],"a":[33,71,77,99,150],"machine":[34],"learning":[35],"based":[36,117],"approach,":[37],"several":[38],"existing":[39],"segmentations":[40,84,90],"from":[41],"10":[42,164],"segmented":[43,145,210],"gold-standard":[44,89],"patients":[45],"are":[46,85,127,209],"learned":[47,68],"by":[48],"random":[49],"decision":[50],"forests":[51],"individually":[52],"collectively.":[54],"The":[55,95,119],"core":[56],"this":[58],"paper":[59],"feature":[61,101],"selection":[62],"application":[65],"classifiers":[69,97],"to":[70,87,103,143,174],"new":[72],"patient":[73],"data":[74],"set.":[75],"In":[76,180],"leave-some-out":[78,160,184],"cross-validation,":[79],"obtained":[81],"full":[82],"compared":[86,173],"untrained":[93],"patients.":[94],"proposed":[96,167],"use":[98],"multi-dimensional":[100],"space":[102],"estimate":[104],"hidden":[106],"truth,":[107],"instead":[108],"relying":[110],"on":[111],"clinical":[112],"standard":[113],"threshold":[114],"connectivity":[116],"methods.":[118,179],"result":[120],"our":[122],"efficient":[123],"whole-body":[124],"section":[125],"classification":[126],"multi-label":[128],"maps":[129],"with":[130,211],"considered":[132],"tissues.":[133],"simulation,":[136],"other":[137],"small":[138],"would":[141],"have":[142],"be":[144],"additionally.":[146],"We":[147],"also":[148],"take":[149],"look":[151],"into":[152],"these":[153],"(liver":[155],"vessels).":[156],"an":[158],"experimental":[159],"study":[161],"consisting":[162],"patients,":[165],"method":[168],"performs":[169],"much":[170],"more":[171],"efficiently":[172],"state":[175],"art":[178],"two":[181],"variants":[182],"experiments":[185],"we":[186],"obtain":[187],"best":[188],"mean":[189],"DICE":[190,212],"ratios":[191],"0.79,":[193],"0.97,":[194],"0.63":[195],"0.83":[197],"for":[198,214,218,223],"hard":[202],"bone":[203],"risk":[205],"structures.":[206],"Liver":[207],"0.93":[213],"liver,":[216],"0.43":[217],"blood":[219],"vessels":[220],"0.39":[222],"bile":[224],"vessels.":[225]},"counts_by_year":[{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}