{"id":"https://openalex.org/W4362604007","doi":"https://doi.org/10.1117/12.2653812","title":"A comparison of U-Net series for CT pancreas segmentation","display_name":"A comparison of U-Net series for CT pancreas segmentation","publication_year":2023,"publication_date":"2023-04-03","ids":{"openalex":"https://openalex.org/W4362604007","doi":"https://doi.org/10.1117/12.2653812"},"language":"en","primary_location":{"id":"doi:10.1117/12.2653812","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Image Processing","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/A5013039259","display_name":"Linya Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linya Zheng","raw_affiliation_strings":["Xiamen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen Univ. (China)","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100421180","display_name":"Ji Li","orcid":"https://orcid.org/0000-0003-4699-084X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Li","raw_affiliation_strings":["Xiamen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen Univ. (China)","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100403566","display_name":"Fan Zhang","orcid":"https://orcid.org/0000-0003-2849-1713"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Zhang","raw_affiliation_strings":["Xiamen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen Univ. (China)","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061938243","display_name":"Hong Shi","orcid":"https://orcid.org/0000-0002-6806-7389"},"institutions":[{"id":"https://openalex.org/I129708740","display_name":"Fujian Medical University","ror":"https://ror.org/050s6ns64","country_code":"CN","type":"education","lineage":["https://openalex.org/I129708740"]},{"id":"https://openalex.org/I4210148548","display_name":"Fujian Provincial Cancer Hospital","ror":"https://ror.org/058ms9w43","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210148548"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Shi","raw_affiliation_strings":["Fujian Cancer Hospital, Fujian Medical Univ. Cancer Hospital (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fujian Cancer Hospital, Fujian Medical Univ. Cancer Hospital (China)","institution_ids":["https://openalex.org/I129708740","https://openalex.org/I4210148548"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082244615","display_name":"Yinran Chen","orcid":"https://orcid.org/0000-0001-8062-1859"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinran Chen","raw_affiliation_strings":["Xiamen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen Univ. (China)","institution_ids":["https://openalex.org/I191208505"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100426934","display_name":"Xi\u00f3ngbi\u0101o Lu\u00f3","orcid":"https://orcid.org/0000-0002-5238-489X"},"institutions":[{"id":"https://openalex.org/I191208505","display_name":"Xiamen University","ror":"https://ror.org/00mcjh785","country_code":"CN","type":"education","lineage":["https://openalex.org/I191208505"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongbiao Luo","raw_affiliation_strings":["Xiamen Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiamen Univ. (China)","institution_ids":["https://openalex.org/I191208505"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"82","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","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/T10036","display_name":"Advanced Neural Network Applications","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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9868999719619751,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9853000044822693,"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/series","display_name":"Series (stratigraphy)","score":0.7063787579536438},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5778729915618896},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.5556634068489075},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45605915784835815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4226521849632263},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3848489224910736},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17740261554718018},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.12447282671928406},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.06198248267173767}],"concepts":[{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.7063787579536438},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5778729915618896},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.5556634068489075},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45605915784835815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4226521849632263},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3848489224910736},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17740261554718018},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.12447282671928406},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.06198248267173767},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2653812","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1117/12.2653812","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2023: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2755342338","https://openalex.org/W1891287906","https://openalex.org/W2036807459","https://openalex.org/W2775347418","https://openalex.org/W1969923398","https://openalex.org/W2772917594","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2058170566"],"abstract_inverted_index":{"The":[0,31],"problems":[1],"of":[2,14,28,101,116,145,159],"the":[3,11,19,23,90,149,157,172,178],"large":[4],"variation":[5],"in":[6,18,37,72],"shape":[7],"and":[8,10,26,46,58,94,128,134,137],"location,":[9],"complex":[12],"background":[13],"many":[15],"neighboring":[16],"tissues":[17],"pancreas":[20,63],"segmentation":[21,45],"hinder":[22],"early":[24],"detection":[25],"diagnosis":[27],"pancreatic":[29],"diseases.":[30],"U-Net":[32,70,111,150],"family":[33],"achieve":[34],"great":[35],"success":[36],"various":[38],"medical":[39],"image":[40],"processing":[41],"tasks":[42],"such":[43],"as":[44],"classification.":[47],"This":[48],"work":[49],"aims":[50],"to":[51,105,166,182],"comparatively":[52],"evaluate":[53],"2D":[54,56,59],"U-Net,":[55],"U-Net++":[57,122],"U-Net3+":[60,86,129],"for":[61],"CT":[62],"segmentation.":[64],"More":[65,147],"interestingly,":[66,148],"We":[67],"also":[68,176],"modify":[69],"series":[71,151],"accordance":[73],"with":[74],"depth":[75],"wise":[76],"separable":[77],"convolution":[78],"(DWC)":[79],"that":[80,110],"replaces":[81],"standard":[82],"convolution.":[83,185],"Without":[84],"DWC,":[85],"works":[87,119],"better":[88,120],"than":[89,121],"other":[91],"two":[92],"networks":[93],"achieves":[95],"an":[96,140],"average":[97,141],"dice":[98,142,179],"similarity":[99,143,180],"coefficient":[100,144],"0.7555.":[102],"Specifically,":[103],"according":[104],"this":[106],"study,":[107],"we":[108],"find":[109],"plus":[112,152],"a":[113],"simple":[114],"module":[115],"DWC":[117,153],"certainly":[118],"using":[123,130,183],"redesigned":[124],"dense":[125],"skip":[126,132],"connections":[127,133],"full-scale":[131],"deep":[135],"supervision":[136],"can":[138,154],"obtain":[139],"0.7613.":[146],"significantly":[155],"reduce":[156],"amount":[158],"training":[160],"parameters":[161],"from":[162],"(39.4M,":[163],"47.2M,":[164],"27.0M)":[165],"(14.3M,":[167],"18.4M,":[168],"3.15M),":[169],"respectively.":[170],"At":[171],"same":[173],"time,":[174],"they":[175],"improve":[177],"compared":[181],"normal":[184]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-22T07:51:19.307946","created_date":"2025-10-10T00:00:00"}