{"id":"https://openalex.org/W4378713684","doi":"https://doi.org/10.1109/access.2023.3281201","title":"A Nested Attention Guided UNet++ Architecture for White Matter Hyperintensity Segmentation","display_name":"A Nested Attention Guided UNet++ Architecture for White Matter Hyperintensity Segmentation","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4378713684","doi":"https://doi.org/10.1109/access.2023.3281201"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3281201","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3281201","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10138414.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10138414.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5092046787","display_name":"Hao Zhang","orcid":"https://orcid.org/0009-0009-3862-2386"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zhang","raw_affiliation_strings":["The Third Affiliated Hospital of Soochow University, Changzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Third Affiliated Hospital of Soochow University, Changzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042268563","display_name":"Chenyang Zhu","orcid":"https://orcid.org/0000-0002-2145-0559"},"institutions":[{"id":"https://openalex.org/I4210153482","display_name":"Changzhou University","ror":"https://ror.org/04ymgwq66","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210153482"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyang Zhu","raw_affiliation_strings":["School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China"],"raw_orcid":"https://orcid.org/0000-0002-2145-0559","affiliations":[{"raw_affiliation_string":"School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou, China","institution_ids":["https://openalex.org/I4210153482"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037498911","display_name":"Xuegan Lian","orcid":"https://orcid.org/0000-0001-9091-7581"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuegan Lian","raw_affiliation_strings":["The Third Affiliated Hospital of Soochow University, Changzhou, China"],"raw_orcid":"https://orcid.org/0009-0003-9319-1095","affiliations":[{"raw_affiliation_string":"The Third Affiliated Hospital of Soochow University, Changzhou, China","institution_ids":["https://openalex.org/I3923682"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100646820","display_name":"Fei Hua","orcid":"https://orcid.org/0000-0001-6958-7979"},"institutions":[{"id":"https://openalex.org/I3923682","display_name":"Soochow University","ror":"https://ror.org/05t8y2r12","country_code":"CN","type":"education","lineage":["https://openalex.org/I3923682"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Hua","raw_affiliation_strings":["The Third Affiliated Hospital of Soochow University, Changzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Third Affiliated Hospital of Soochow University, Changzhou, China","institution_ids":["https://openalex.org/I3923682"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.1689,"has_fulltext":true,"cited_by_count":11,"citation_normalized_percentile":{"value":0.80509045,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"66910","last_page":"66920"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9914000034332275,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9914000034332275,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.989300012588501,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9708999991416931,"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/segmentation","display_name":"Segmentation","score":0.7652366757392883},{"id":"https://openalex.org/keywords/atlas","display_name":"Atlas (anatomy)","score":0.7208797931671143},{"id":"https://openalex.org/keywords/hyperintensity","display_name":"Hyperintensity","score":0.7202422022819519},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6779797077178955},{"id":"https://openalex.org/keywords/fluid-attenuated-inversion-recovery","display_name":"Fluid-attenuated inversion recovery","score":0.6537177562713623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6398716568946838},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5057389736175537},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4904153048992157},{"id":"https://openalex.org/keywords/nested-set-model","display_name":"Nested set model","score":0.46270421147346497},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3732471466064453},{"id":"https://openalex.org/keywords/magnetic-resonance-imaging","display_name":"Magnetic resonance imaging","score":0.3515123724937439},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.15798726677894592},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.1340835690498352},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.11025163531303406},{"id":"https://openalex.org/keywords/relational-database","display_name":"Relational database","score":0.08115625381469727}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7652366757392883},{"id":"https://openalex.org/C2776673561","wikidata":"https://www.wikidata.org/wiki/Q655357","display_name":"Atlas (anatomy)","level":2,"score":0.7208797931671143},{"id":"https://openalex.org/C146638467","wikidata":"https://www.wikidata.org/wiki/Q10529587","display_name":"Hyperintensity","level":3,"score":0.7202422022819519},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6779797077178955},{"id":"https://openalex.org/C101070640","wikidata":"https://www.wikidata.org/wiki/Q3737215","display_name":"Fluid-attenuated inversion recovery","level":3,"score":0.6537177562713623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6398716568946838},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5057389736175537},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4904153048992157},{"id":"https://openalex.org/C103000020","wikidata":"https://www.wikidata.org/wiki/Q1978426","display_name":"Nested set model","level":3,"score":0.46270421147346497},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3732471466064453},{"id":"https://openalex.org/C143409427","wikidata":"https://www.wikidata.org/wiki/Q161238","display_name":"Magnetic resonance imaging","level":2,"score":0.3515123724937439},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.15798726677894592},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.1340835690498352},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.11025163531303406},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.08115625381469727},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3281201","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3281201","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10138414.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:efc5649fc4a847d0802aea99f8fa7270","is_oa":true,"landing_page_url":"https://doaj.org/article/efc5649fc4a847d0802aea99f8fa7270","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 66910-66920 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3281201","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3281201","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10138414.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4378713684.pdf","grobid_xml":"https://content.openalex.org/works/W4378713684.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W575146699","https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1993947467","https://openalex.org/W2049791419","https://openalex.org/W2123605592","https://openalex.org/W2133287637","https://openalex.org/W2462030629","https://openalex.org/W2519621290","https://openalex.org/W2532750509","https://openalex.org/W2563179587","https://openalex.org/W2612655633","https://openalex.org/W2618530766","https://openalex.org/W2622826443","https://openalex.org/W2789256791","https://openalex.org/W2795554802","https://openalex.org/W2798122215","https://openalex.org/W2884239853","https://openalex.org/W2884436604","https://openalex.org/W2952735543","https://openalex.org/W2953949094","https://openalex.org/W2954623871","https://openalex.org/W2964098128","https://openalex.org/W2965013858","https://openalex.org/W2979378912","https://openalex.org/W2979615324","https://openalex.org/W2982140522","https://openalex.org/W3100991048","https://openalex.org/W3119458123","https://openalex.org/W3164956625","https://openalex.org/W3196912198","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6750469568","https://openalex.org/W6753182481","https://openalex.org/W6784666524"],"related_works":["https://openalex.org/W4362605343","https://openalex.org/W4310330293","https://openalex.org/W2517104666","https://openalex.org/W2005437358","https://openalex.org/W1669643531","https://openalex.org/W2008656436","https://openalex.org/W2134924024","https://openalex.org/W2023558673","https://openalex.org/W2110230079","https://openalex.org/W1982826852"],"abstract_inverted_index":{"White":[0],"Matter":[1],"Hyperintensity":[2],"(WMH)":[3],"is":[4,19,31,145],"a":[5,95],"common":[6],"finding":[7],"in":[8,60],"Magnetic":[9],"Resonance":[10],"Imaging":[11],"(MRI)":[12],"of":[13,28,39,45,58,110,121,162,191],"patients":[14],"with":[15,21],"cerebral":[16],"infarction":[17],"and":[18,25,41,107,128,179,188],"associated":[20],"poor":[22],"prognosis.":[23],"Accurate":[24],"rapid":[26],"segmentation":[27,49,160,193],"WMH":[29,59,111],"lesions":[30,112],"critical":[32],"for":[33,116,150],"clinicians":[34],"to":[35,54,63,104,196],"assess":[36],"the":[37,42,55,73,124,129,140,148,151,159,163,170,183,192,197],"risk":[38],"rebleeding":[40],"long-term":[43],"prognosis":[44],"thrombolytic":[46],"patients.":[47],"However,":[48],"can":[50],"be":[51],"challenging":[52],"due":[53],"erratic":[56],"signals":[57],"MRI,":[61],"leading":[62],"imprecise":[64],"results.":[65],"Deep":[66],"learning-based":[67],"approaches":[68],"have":[69],"been":[70],"proposed,":[71],"but":[72],"dice":[74],"similarity":[75],"coefficient":[76],"remains":[77],"low.":[78],"Atlas":[79],"images":[80,115],"are":[81],"navigation":[82],"maps":[83],"that":[84,100,157,169],"integrate":[85],"various":[86],"medical":[87],"information":[88],"expressions.":[89],"In":[90],"this":[91],"study,":[92],"we":[93],"propose":[94],"nested":[96,130,132,152,154,184],"attention-guided":[97,131,153,198],"UNet++":[98,180],"framework":[99,119,173],"employs":[101],"attention":[102,126,137,142],"mechanisms":[103],"capture":[105],"local":[106],"global":[108],"features":[109],"using":[113],"atlas":[114,125,136,141],"segmentation.":[117],"The":[118,135],"consists":[120],"two":[122],"modules,":[123],"module,":[127],"U-Net":[133,155],"module.":[134],"module":[138,156],"generates":[139,158],"map,":[143],"which":[144],"used":[146],"as":[147],"input":[149],"map":[161],"FLAIR":[164],"image.":[165],"Experimental":[166],"results":[167,194],"demonstrate":[168],"proposed":[171],"NAUNet++":[172],"converges":[174],"faster":[175],"than":[176],"conventional":[177],"UNet":[178],"approaches.":[181],"Moreover,":[182],"architecture":[185],"enhances":[186],"recall":[187],"f1":[189],"scores":[190],"compared":[195],"approach.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T08:27:34.040176","created_date":"2023-05-30T00:00:00"}
