{"id":"https://openalex.org/W2793028257","doi":"https://doi.org/10.1117/12.2292181","title":"Nearest neighbor 3D segmentation with context features","display_name":"Nearest neighbor 3D segmentation with context features","publication_year":2018,"publication_date":"2018-03-02","ids":{"openalex":"https://openalex.org/W2793028257","doi":"https://doi.org/10.1117/12.2292181","mag":"2793028257"},"language":"en","primary_location":{"id":"doi:10.1117/12.2292181","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2292181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","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/A5064304390","display_name":"Mattias P. Heinrich\u202c","orcid":"https://orcid.org/0000-0002-7489-1972"},"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":true,"raw_author_name":"Mattias P. Heinrich","raw_affiliation_strings":["Univ. zu L\u00fcbeck (Germany)"],"affiliations":[{"raw_affiliation_string":"Univ. zu L\u00fcbeck (Germany)","institution_ids":["https://openalex.org/I9341345"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041524969","display_name":"Hannes Nickisch","orcid":"https://orcid.org/0000-0003-1604-6647"},"institutions":[{"id":"https://openalex.org/I4210162505","display_name":"Philips (Germany)","ror":"https://ror.org/05san5604","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210122849","https://openalex.org/I4210162505"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hannes Nickisch","raw_affiliation_strings":["Philips Research (Germany)"],"affiliations":[{"raw_affiliation_string":"Philips Research (Germany)","institution_ids":["https://openalex.org/I4210162505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5098179651","display_name":"Hristova Evelin","orcid":null},"institutions":[{"id":"https://openalex.org/I4210162505","display_name":"Philips (Germany)","ror":"https://ror.org/05san5604","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210122849","https://openalex.org/I4210162505"]},{"id":"https://openalex.org/I159176309","display_name":"Universit\u00e4t Hamburg","ror":"https://ror.org/00g30e956","country_code":"DE","type":"education","lineage":["https://openalex.org/I159176309"]},{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Hristova Evelin","raw_affiliation_strings":["Hamburg Univ. of Applied Sciences (Germany)","Philips Research (Germany)"],"affiliations":[{"raw_affiliation_string":"Hamburg Univ. of Applied Sciences (Germany)","institution_ids":["https://openalex.org/I70451448","https://openalex.org/I159176309"]},{"raw_affiliation_string":"Philips Research (Germany)","institution_ids":["https://openalex.org/I4210162505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030993068","display_name":"Heinrich Schulz","orcid":"https://orcid.org/0000-0003-4048-2254"},"institutions":[{"id":"https://openalex.org/I4210162505","display_name":"Philips (Germany)","ror":"https://ror.org/05san5604","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210122849","https://openalex.org/I4210162505"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Heinrich Schulz","raw_affiliation_strings":["Philips Research (Germany)"],"affiliations":[{"raw_affiliation_string":"Philips Research (Germany)","institution_ids":["https://openalex.org/I4210162505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055312613","display_name":"Tom Brosch","orcid":"https://orcid.org/0000-0002-3543-1930"},"institutions":[{"id":"https://openalex.org/I4210162505","display_name":"Philips (Germany)","ror":"https://ror.org/05san5604","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210122849","https://openalex.org/I4210162505"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tom Brosch","raw_affiliation_strings":["Philips Research (Germany)"],"affiliations":[{"raw_affiliation_string":"Philips Research (Germany)","institution_ids":["https://openalex.org/I4210162505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5064304390"],"corresponding_institution_ids":["https://openalex.org/I9341345"],"apc_list":null,"apc_paid":null,"fwci":0.1045,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.41092888,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"21","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":1.0,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":1.0,"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/T10824","display_name":"Image Retrieval and Classification 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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/computer-science","display_name":"Computer science","score":0.6242702603340149},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.550276517868042},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.5368220806121826},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5341889262199402},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.478950560092926},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39528053998947144},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15359416604042053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6242702603340149},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.550276517868042},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.5368220806121826},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5341889262199402},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.478950560092926},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39528053998947144},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15359416604042053},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2292181","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2292181","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2018: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.46000000834465027}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W1491719799","https://openalex.org/W2032377318","https://openalex.org/W2049644877","https://openalex.org/W2086504823","https://openalex.org/W2097921974","https://openalex.org/W2146020873","https://openalex.org/W2157853947","https://openalex.org/W2163808566","https://openalex.org/W2165428671","https://openalex.org/W2171963641","https://openalex.org/W2524186126","https://openalex.org/W6629564929","https://openalex.org/W6674878074","https://openalex.org/W6682020166","https://openalex.org/W6683751579","https://openalex.org/W6727600152"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W2785302227","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W2033914206","https://openalex.org/W2042327336","https://openalex.org/W2062957446","https://openalex.org/W2147397890","https://openalex.org/W2166213322"],"abstract_inverted_index":{"Automated":[0],"and":[1,10,31,63,65,78,102,117,133,165],"fast":[2],"multi-label":[3],"segmentation":[4,98,148],"of":[5,44,93,99,113,126,130,145],"medical":[6],"images":[7,40],"is":[8,58,71,104,119],"challenging":[9],"clinically":[11],"important.":[12],"This":[13],"paper":[14],"builds":[15],"upon":[16],"a":[17,153],"supervised":[18],"machine":[19],"learning":[20],"framework":[21],"that":[22],"uses":[23],"training":[24],"data":[25,164],"sets":[26],"with":[27],"dense":[28],"organ":[29],"annotations":[30],"vantage":[32],"point":[33],"trees":[34],"to":[35,60,85,140],"classify":[36],"voxels":[37],"in":[38],"unseen":[39],"based":[41],"on":[42,74],"similarity":[43],"binary":[45],"feature":[46],"vectors":[47],"extracted":[48],"from":[49],"the":[50,56,96,110,127,131,135],"data.":[51],"Without":[52],"explicit":[53],"model":[54],"knowledge,":[55],"algorithm":[57],"applicable":[59],"different":[61],"modalities":[62],"organs,":[64],"achieves":[66],"high":[67],"accuracy.":[68,146],"The":[69,106,147],"method":[70,132,156],"successfully":[72],"tested":[73],"70":[75],"abdominal":[76],"CT":[77,97],"42":[79],"pelvic":[80],"MR":[81,111],"images.":[82],"With":[83],"respect":[84],"ground":[86],"truth,":[87],"an":[88],"average":[89],"Dice":[90],"overlap":[91],"score":[92,108],"0.76":[94],"for":[95,109,152],"liver,":[100],"spleen":[101],"kidneys":[103],"achieved.":[105],"mean":[107],"delineation":[112],"bladder,":[114],"bones,":[115],"prostate":[116],"rectum":[118],"0.65.":[120],"Additionally,":[121],"we":[122],"benchmark":[123],"several":[124],"variations":[125],"main":[128],"components":[129],"reduce":[134],"computation":[136],"time":[137,166],"by":[138],"up":[139],"47%":[141],"without":[142],"significant":[143],"loss":[144],"results":[149],"are":[150],"–":[151,157],"nearest":[154],"neighbor":[155],"surprisingly":[158],"accurate,":[159],"robust":[160],"as":[161,163],"well":[162],"efficient.":[167]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}