{"id":"https://openalex.org/W4406238150","doi":"https://doi.org/10.1109/bibm62325.2024.10822149","title":"SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation","display_name":"SACNet: A Spatially Adaptive Convolution Network for 2D Multi-organ Medical Segmentation","publication_year":2024,"publication_date":"2024-12-03","ids":{"openalex":"https://openalex.org/W4406238150","doi":"https://doi.org/10.1109/bibm62325.2024.10822149"},"language":"en","primary_location":{"id":"doi:10.1109/bibm62325.2024.10822149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5008721999","display_name":"Lin Zhang","orcid":"https://orcid.org/0000-0003-2002-1944"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lin Zhang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101782805","display_name":"Wenbo Gao","orcid":"https://orcid.org/0000-0001-6424-5814"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Gao","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016351381","display_name":"Jie Yi","orcid":"https://orcid.org/0000-0003-2425-0920"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Yi","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5090906323","display_name":"Yunyun Yang","orcid":"https://orcid.org/0000-0002-0488-7652"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yunyun Yang","raw_affiliation_strings":["Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Shenzhen,School of Science,Shenzhen,China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008721999"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23636811,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1748","last_page":"1751"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9915000200271606,"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"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9915000200271606,"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/T10862","display_name":"AI in cancer detection","score":0.9909999966621399,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9890000224113464,"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/computer-science","display_name":"Computer science","score":0.7908899784088135},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7502512335777283},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.674198567867279},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5531175136566162},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49469229578971863},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4519517123699188},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12718713283538818}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7908899784088135},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7502512335777283},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.674198567867279},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5531175136566162},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49469229578971863},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4519517123699188},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12718713283538818}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm62325.2024.10822149","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm62325.2024.10822149","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321921","display_name":"Natural Science Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W2601564443","https://openalex.org/W2734349601","https://openalex.org/W2884436604","https://openalex.org/W2889640697","https://openalex.org/W2928133111","https://openalex.org/W2966926453","https://openalex.org/W3014974815","https://openalex.org/W3082584514","https://openalex.org/W3123982987","https://openalex.org/W3174708193","https://openalex.org/W3197957534","https://openalex.org/W3212933375","https://openalex.org/W4206693420","https://openalex.org/W4212875960","https://openalex.org/W4281784192","https://openalex.org/W4283815817","https://openalex.org/W4293070934","https://openalex.org/W4296425595","https://openalex.org/W4312568229","https://openalex.org/W4312794844","https://openalex.org/W4312960790","https://openalex.org/W4319300502","https://openalex.org/W4321232185","https://openalex.org/W4382877880","https://openalex.org/W4386076222","https://openalex.org/W4387430177","https://openalex.org/W4396973073"],"related_works":["https://openalex.org/W2772917594","https://openalex.org/W2036807459","https://openalex.org/W2058170566","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W2229312674","https://openalex.org/W2951359407","https://openalex.org/W2079911747","https://openalex.org/W1969923398"],"abstract_inverted_index":{"Multi-organ":[0],"segmentation":[1,44,69,197,201],"in":[2,21,53,183,199],"medical":[3],"image":[4],"analysis":[5],"is":[6],"crucial":[7],"for":[8],"diagnosis":[9],"and":[10,25,42,60,89,128,135,151,155,173,180],"treatment":[11],"planning.":[12],"However,":[13],"many":[14],"factors":[15],"complicate":[16],"the":[17,35,65,74,99,108,124,133,138],"task,":[18],"including":[19],"variability":[20],"different":[22,68,103],"target":[23],"categories":[24],"interference":[26],"from":[27,190],"complex":[28,181],"backgrounds.":[29],"In":[30],"this":[31],"paper,":[32],"we":[33,72,116,159],"utilize":[34,117],"knowledge":[36],"of":[37,67,86,102,126],"Deformable":[38],"Convolution":[39,50],"V3":[40],"(DCNv3)":[41],"multi-object":[43],"to":[45,93,112,122,176,204],"optimize":[46],"our":[47],"Spatially":[48],"Adaptive":[49,75],"Network":[51],"(SACNet)":[52],"three":[54],"aspects:":[55],"feature":[56],"extraction,":[57],"model":[58],"architecture,":[59],"loss":[61,166,172],"constraint,":[62],"simultaneously":[63],"enhancing":[64],"perception":[66],"targets.":[70,114],"Firstly,":[71],"propose":[73,160],"Receptive":[76],"Field":[77],"Module":[78],"(ARFM),":[79],"which":[80],"combines":[81],"DCNv3":[82],"with":[83],"a":[84,147,152,161],"series":[85],"customized":[87],"block-level":[88],"architecture-level":[90],"designs":[91],"similar":[92],"transformers.":[94],"This":[95,144],"module":[96],"can":[97],"capture":[98],"unique":[100],"features":[101],"organs":[104],"by":[105],"adaptively":[106],"adjusting":[107],"receptive":[109],"field":[110],"according":[111],"various":[113],"Secondly,":[115],"ARFM":[118],"as":[119],"building":[120],"blocks":[121],"construct":[123],"encoder-decoder":[125],"SACNet":[127,194],"partially":[129],"share":[130],"parameters":[131],"between":[132],"encoder":[134],"decoder,":[136],"making":[137],"network":[139],"wider":[140],"rather":[141],"than":[142],"deeper.":[143],"design":[145],"achieves":[146],"shared":[148],"lightweight":[149],"decoder":[150],"more":[153],"parameter-efficient":[154],"effective":[156],"framework.":[157],"Lastly,":[158],"novel":[162],"continuity":[163],"dynamic":[164],"adjustment":[165],"function,":[167],"based":[168],"on":[169,186],"t-vMF":[170],"dice":[171],"cross-entropy":[174],"loss,":[175],"better":[177],"balance":[178],"easy":[179],"classes":[182],"segmentation.":[184],"Experiments":[185],"3D":[187],"slice":[188],"datasets":[189],"Synapse":[191],"demonstrate":[192],"that":[193],"delivers":[195],"superior":[196],"performance":[198],"multi-organ":[200],"tasks":[202],"compared":[203],"several":[205],"existing":[206],"methods.":[207]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
