{"id":"https://openalex.org/W3120077440","doi":"https://doi.org/10.1145/3431943.3432287","title":"Brain Image Parcellation Using Fully Convolutional Network with Adaptively Selected Features from Brain Atlases","display_name":"Brain Image Parcellation Using Fully Convolutional Network with Adaptively Selected Features from Brain Atlases","publication_year":2020,"publication_date":"2020-10-16","ids":{"openalex":"https://openalex.org/W3120077440","doi":"https://doi.org/10.1145/3431943.3432287","mag":"3120077440"},"language":"en","primary_location":{"id":"doi:10.1145/3431943.3432287","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431943.3432287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 9th International Conference on Bioinformatics and Biomedical Science","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/A5100703214","display_name":"Xiao Zhang","orcid":"https://orcid.org/0000-0002-6413-6148"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiao Zhang","raw_affiliation_strings":["Anhui University, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102009463","display_name":"Haifeng Zhao","orcid":"https://orcid.org/0000-0002-5300-0683"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haifeng Zhao","raw_affiliation_strings":["Anhui University, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101553435","display_name":"Zhenyu Tang","orcid":"https://orcid.org/0000-0002-6998-2669"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenyu Tang","raw_affiliation_strings":["Beihang University, China"],"affiliations":[{"raw_affiliation_string":"Beihang University, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100768911","display_name":"Shaojie Zhang","orcid":"https://orcid.org/0000-0002-0406-3290"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shaojie Zhang","raw_affiliation_strings":["Anhui University, China"],"affiliations":[{"raw_affiliation_string":"Anhui University, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100703214"],"corresponding_institution_ids":["https://openalex.org/I143868143"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20781993,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"107","last_page":"111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9998000264167786,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9987000226974487,"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/computer-science","display_name":"Computer science","score":0.7475648522377014},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7086000442504883},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5251985192298889},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4479677677154541},{"id":"https://openalex.org/keywords/brain-atlas","display_name":"Brain atlas","score":0.41349726915359497},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.37912222743034363}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7475648522377014},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7086000442504883},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5251985192298889},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4479677677154541},{"id":"https://openalex.org/C2780972224","wikidata":"https://www.wikidata.org/wiki/Q17047956","display_name":"Brain atlas","level":2,"score":0.41349726915359497},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.37912222743034363}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3431943.3432287","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3431943.3432287","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 9th International Conference on Bioinformatics and Biomedical Science","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1884191083","https://openalex.org/W1987869189","https://openalex.org/W2018662705","https://openalex.org/W2083099567","https://openalex.org/W2160905891","https://openalex.org/W2892151509","https://openalex.org/W2899478482","https://openalex.org/W2924675021","https://openalex.org/W2952632681","https://openalex.org/W2963091558","https://openalex.org/W4233415207","https://openalex.org/W4243153816"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W2058170566","https://openalex.org/W2772917594","https://openalex.org/W2755342338","https://openalex.org/W2166024367","https://openalex.org/W3116076068","https://openalex.org/W3009238340","https://openalex.org/W2229312674","https://openalex.org/W2951359407"],"abstract_inverted_index":{"Brain":[0],"image":[1,44,135,176],"parcellation":[2,14,32,45,87,147],"is":[3,101,113,131,149],"an":[4,108],"important":[5],"data":[6],"processing":[7],"step":[8],"in":[9,68,107],"neuroscience.":[10],"Since":[11],"multi-atlas":[12],"based":[13,42,114,120],"(MAP)":[15],"uses":[16],"prior":[17],"information":[18],"from":[19],"brain":[20,25,31,43,79,105,134,143,159,175,196],"atlases":[21,106,160],"(i.e.,":[22],"manually":[23],"labeled":[24],"regions),":[26],"it":[27,65,82],"can":[28],"provide":[29],"accurate":[30],"and":[33,117,145,179,185],"has":[34,60],"been":[35,54],"widely":[36],"adopted.":[37],"Recently,":[38],"some":[39],"deep":[40],"learning":[41],"(DLP)":[46],"methods":[47,73,189],"using":[48,171],"fully":[49],"convolutional":[50],"network":[51,112,130],"(FCN)":[52],"have":[53],"proposed.":[55],"Compared":[56],"with":[57],"MAP,":[58],"DLP":[59,72,98,188],"high":[61],"computational":[62],"efficiency,":[63],"making":[64,81],"more":[66],"applicable":[67],"practice.":[69],"However,":[70],"existing":[71],"either":[74],"neglect":[75],"or":[76],"partially":[77],"utilize":[78],"atlases,":[80,144],"difficult":[83],"to":[84,103,136,191],"get":[85],"comparable":[86],"accuracy":[88],"as":[89,139,141],"MAP.":[90],"In":[91,168],"this":[92],"paper,":[93],"we":[94],"propose":[95],"a":[96],"new":[97],"method":[99,182],"which":[100],"able":[102],"use":[104],"effective":[109,193],"way.":[110],"The":[111,126],"on":[115],"FCN":[116,153],"non-local":[118],"block":[119],"channel":[121],"attention":[122],"module":[123],"(NL":[124],"module).":[125],"input":[127],"of":[128,158,195],"our":[129,181],"the":[132,146,152,156,169,186,192],"target":[133],"be":[137],"parcellated":[138],"well":[140],"available":[142],"result":[148],"produced":[150],"through":[151],"guided":[154],"by":[155,162],"features":[157],"selected":[161],"NL":[163],"modules":[164],"at":[165],"different":[166],"scales.":[167],"experiments":[170],"two":[172],"public":[173],"MR":[174],"datasets":[177],"(LPBA40":[178],"NIREP-NA0),":[180],"outperforms":[183],"MAP":[184],"state-of-the-art":[187],"due":[190],"usage":[194],"atlases.":[197]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
