{"id":"https://openalex.org/W3200914054","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534007","title":"Kidney segmentation from computed tomography images using U-Net and batch-based synthesis","display_name":"Kidney segmentation from computed tomography images using U-Net and batch-based synthesis","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3200914054","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534007","mag":"3200914054"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","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/A5064563538","display_name":"Tomasz Le\u015b","orcid":"https://orcid.org/0000-0001-6131-9080"},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":true,"raw_author_name":"Tomasz Les","raw_affiliation_strings":["Warsaw University of Technology, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Warsaw University of Technology, Warsaw, Poland","institution_ids":["https://openalex.org/I108403487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058080492","display_name":"Tomasz Markiewcz","orcid":null},"institutions":[{"id":"https://openalex.org/I108403487","display_name":"Warsaw University of Technology","ror":"https://ror.org/00y0xnp53","country_code":"PL","type":"education","lineage":["https://openalex.org/I108403487"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Tomasz Markiewcz","raw_affiliation_strings":["Warsaw University of Technology, Military Institute of Medicine, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Warsaw University of Technology, Military Institute of Medicine, Warsaw, Poland","institution_ids":["https://openalex.org/I108403487"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084562393","display_name":"Miros\u0142aw Dziekiewicz","orcid":"https://orcid.org/0000-0003-2504-6462"},"institutions":[{"id":"https://openalex.org/I4210121233","display_name":"Wojskowy Instytut Medycyny Lotniczej","ror":"https://ror.org/01xtcza13","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210121233"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Miroslaw Dziekiewicz","raw_affiliation_strings":["Military Institute of Medicine, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Military Institute of Medicine, Warsaw, Poland","institution_ids":["https://openalex.org/I4210121233"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5076585902","display_name":"Ma\u0142gorzata Lorent","orcid":null},"institutions":[{"id":"https://openalex.org/I4210121233","display_name":"Wojskowy Instytut Medycyny Lotniczej","ror":"https://ror.org/01xtcza13","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210121233"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Malgorzata Lorent","raw_affiliation_strings":["Military Institute of Medicine, Warsaw, Poland"],"affiliations":[{"raw_affiliation_string":"Military Institute of Medicine, Warsaw, Poland","institution_ids":["https://openalex.org/I4210121233"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5064563538"],"corresponding_institution_ids":["https://openalex.org/I108403487"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.1169281,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"11045","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9873999953269958,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9873999953269958,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9868000149726868,"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":0.95660001039505,"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/segmentation","display_name":"Segmentation","score":0.7351981401443481},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7024088501930237},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6578978300094604},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5510134696960449},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5382525324821472},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5274460911750793},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.5088129639625549},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49345579743385315},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.42432767152786255},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.09513875842094421},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09056854248046875}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7351981401443481},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7024088501930237},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6578978300094604},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5510134696960449},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5382525324821472},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5274460911750793},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.5088129639625549},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49345579743385315},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.42432767152786255},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.09513875842094421},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09056854248046875},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534007","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534007","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Partnerships for the goals","id":"https://metadata.un.org/sdg/17","score":0.4099999964237213}],"awards":[{"id":"https://openalex.org/G7896011958","display_name":null,"funder_award_id":"2016/23/B/ST6/00621","funder_id":"https://openalex.org/F4320322511","funder_display_name":"Narodowe Centrum Nauki"}],"funders":[{"id":"https://openalex.org/F4320322511","display_name":"Narodowe Centrum Nauki","ror":"https://ror.org/03ha2q922"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1538131130","https://openalex.org/W1575387439","https://openalex.org/W1757407923","https://openalex.org/W2135139265","https://openalex.org/W2151103935","https://openalex.org/W2478157071","https://openalex.org/W2546818739","https://openalex.org/W2613326680","https://openalex.org/W2743654837","https://openalex.org/W2884436604","https://openalex.org/W2944539652","https://openalex.org/W2951166644","https://openalex.org/W2977631783","https://openalex.org/W2995989004","https://openalex.org/W3017774855","https://openalex.org/W3090605478","https://openalex.org/W3091528702","https://openalex.org/W3094830201","https://openalex.org/W3111817041","https://openalex.org/W6632100814","https://openalex.org/W6753182481"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4324315429","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4366829857","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W2799953226","https://openalex.org/W2070902896","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0,13,30,43,78,110],"article":[1],"presents":[2,32],"an":[3,55,139],"innovative":[4],"technique":[5],"of":[6,151,158],"kidney":[7,52,98,165],"detection":[8,166],"in":[9,25,170],"computed":[10,171],"tomography":[11,172],"images.":[12,173],"proposed":[14,44],"system":[15,39,80,169],"is":[16,48,155],"based":[17,57,122,142],"on":[18,58,123,143],"a":[19],"batch-based":[20],"synthesis":[21],"algorithm,":[22],"never":[23],"used":[24],"medical":[26,90],"image":[27,41,56,61,70,73,145],"processing":[28],"before.":[29],"manuscript":[31],"key":[33],"points:":[34],"U-Net-based":[35],"initial":[36],"segmentation,":[37],"tracking":[38],"and":[40,63,71,114,136,161,167],"completion.":[42],"system's":[45,111],"main":[46,129],"concept":[47,157],"to":[49,83,119],"remove":[50],"the":[51,59,65,68,72,75,84,152,156,159],"region":[53],"from":[54,89],"previous":[60],"mask":[62],"calculate":[64],"difference":[66],"between":[67],"original":[69,160],"with":[74,138],"removed":[76],"kidney.":[77],"developed":[79,153],"was":[81],"compared":[82,118],"patterns":[85],"obtained":[86],"by":[87],"specialists":[88],"centers.":[91],"Numerical":[92,131],"tests":[93],"were":[94],"carried":[95],"out":[96],"confirming":[97],"detection's":[99],"high":[100,112],"effectiveness,":[101],"which":[102],"equals":[103],"<tex":[104],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[105],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$90.63\\pm":[106],"4.59\\":[107],"\\%$</tex>":[108],".":[109],"performance":[113,134],"low":[115],"hardware":[116],"requirements":[117],"typical":[120],"techniques":[121,154],"deep":[124],"learning":[125],"systems":[126],"are":[127],"its":[128],"advantages.":[130],"experiments":[132],"supplied":[133],"analysis":[135],"comparison":[137],"alternative":[140],"solution":[141],"U-Net":[144],"semantic":[146],"segmentation.":[147],"A":[148],"measurable":[149],"effect":[150],"practical":[162],"fully":[163],"automatic":[164],"segmentation":[168]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}