{"id":"https://openalex.org/W4282943709","doi":"https://doi.org/10.1109/jbhi.2022.3178765","title":"Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells","display_name":"Deep Features Aggregation-Based Joint Segmentation of Cytoplasm and Nuclei in White Blood Cells","publication_year":2022,"publication_date":"2022-05-30","ids":{"openalex":"https://openalex.org/W4282943709","doi":"https://doi.org/10.1109/jbhi.2022.3178765","pmid":"https://pubmed.ncbi.nlm.nih.gov/35635825"},"language":"en","primary_location":{"id":"doi:10.1109/jbhi.2022.3178765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3178765","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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 Journal of Biomedical and Health Informatics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5050153883","display_name":"Adnan Haider","orcid":"https://orcid.org/0000-0003-2240-2708"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Adnan Haider","raw_affiliation_strings":["Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0003-2240-2708","affiliations":[{"raw_affiliation_string":"Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080344855","display_name":"Muhammad Arsalan","orcid":"https://orcid.org/0000-0003-1868-5207"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Arsalan","raw_affiliation_strings":["Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0003-1868-5207","affiliations":[{"raw_affiliation_string":"Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029554802","display_name":"Young Won Lee","orcid":"https://orcid.org/0000-0003-3253-7593"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Young Won Lee","raw_affiliation_strings":["Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083173630","display_name":"Kang Ryoung Park","orcid":"https://orcid.org/0000-0002-1214-9510"},"institutions":[{"id":"https://openalex.org/I205490536","display_name":"Dongguk University","ror":"https://ror.org/057q6n778","country_code":"KR","type":"education","lineage":["https://openalex.org/I205490536"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kang Ryoung Park","raw_affiliation_strings":["Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea"],"raw_orcid":"https://orcid.org/0000-0002-1214-9510","affiliations":[{"raw_affiliation_string":"Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Korea","institution_ids":["https://openalex.org/I205490536"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5050153883"],"corresponding_institution_ids":["https://openalex.org/I205490536"],"apc_list":null,"apc_paid":null,"fwci":2.1429,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.88649567,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"26","issue":"8","first_page":"3685","last_page":"3696"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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/T12874","display_name":"Digital Imaging for Blood Diseases","score":1.0,"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/T11688","display_name":"Microbial infections and disease research","score":0.9641000032424927,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9634000062942505,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7571878433227539},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7304622530937195},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.683609664440155},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6426937580108643},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6137193441390991},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5490846037864685},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.45727258920669556},{"id":"https://openalex.org/keywords/digital-pathology","display_name":"Digital pathology","score":0.42697176337242126},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.4228222966194153}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7571878433227539},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7304622530937195},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.683609664440155},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6426937580108643},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6137193441390991},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5490846037864685},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.45727258920669556},{"id":"https://openalex.org/C2777522853","wikidata":"https://www.wikidata.org/wiki/Q5276128","display_name":"Digital pathology","level":2,"score":0.42697176337242126},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.4228222966194153},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C170154142","wikidata":"https://www.wikidata.org/wiki/Q150737","display_name":"Architectural engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0}],"mesh":[{"descriptor_ui":"D003593","descriptor_name":"Cytoplasm","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003593","descriptor_name":"Cytoplasm","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D003593","descriptor_name":"Cytoplasm","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007091","descriptor_name":"Image Processing, Computer-Assisted","qualifier_ui":"Q000379","qualifier_name":"methods","is_major_topic":false},{"descriptor_ui":"D007962","descriptor_name":"Leukocytes","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007962","descriptor_name":"Leukocytes","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D007962","descriptor_name":"Leukocytes","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1109/jbhi.2022.3178765","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jbhi.2022.3178765","pdf_url":null,"source":{"id":"https://openalex.org/S2495854775","display_name":"IEEE Journal of Biomedical and Health Informatics","issn_l":"2168-2194","issn":["2168-2194","2168-2208"],"is_oa":false,"is_in_doaj":false,"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 Journal of Biomedical and Health Informatics","raw_type":"journal-article"},{"id":"pmid:35635825","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/35635825","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE journal of biomedical and health informatics","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5699999928474426,"id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G5175326530","display_name":null,"funder_award_id":"NRF-2021R1F1A1045587","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"},{"id":"https://openalex.org/F4320328359","display_name":"Ministry of Science and ICT, South Korea","ror":"https://ror.org/01wpjm123"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W1515472966","https://openalex.org/W1522301498","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1980276039","https://openalex.org/W2185824146","https://openalex.org/W2578827697","https://openalex.org/W2589644515","https://openalex.org/W2762113702","https://openalex.org/W2767426931","https://openalex.org/W2782757030","https://openalex.org/W2793002108","https://openalex.org/W2808647902","https://openalex.org/W2884436604","https://openalex.org/W2959123891","https://openalex.org/W2960903270","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2963990934","https://openalex.org/W2970005570","https://openalex.org/W2973003726","https://openalex.org/W2989874578","https://openalex.org/W2991273604","https://openalex.org/W2995044275","https://openalex.org/W2998825217","https://openalex.org/W3011178854","https://openalex.org/W3012682715","https://openalex.org/W3035974373","https://openalex.org/W3037839692","https://openalex.org/W3061357073","https://openalex.org/W3082931514","https://openalex.org/W3101053323","https://openalex.org/W3103686361","https://openalex.org/W3111045851","https://openalex.org/W3119831909","https://openalex.org/W3165420968","https://openalex.org/W3171092390","https://openalex.org/W3203232043","https://openalex.org/W3213222106","https://openalex.org/W4205428558","https://openalex.org/W6631190155","https://openalex.org/W6639824700","https://openalex.org/W6725739302","https://openalex.org/W6747680804","https://openalex.org/W6765590825","https://openalex.org/W6795780754","https://openalex.org/W6796260538","https://openalex.org/W6805359719"],"related_works":["https://openalex.org/W1975767702","https://openalex.org/W4293226380","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W4315434538","https://openalex.org/W3165349357"],"abstract_inverted_index":{"White":[0],"blood":[1,16,29],"cells":[2],"(WBCs),":[3],"also":[4],"known":[5],"as":[6],"leukocytes,":[7],"are":[8,181],"one":[9],"of":[10,14,28,66,91,110,157,177],"the":[11,15,25,63,107,111,150,154,174],"valuable":[12],"parts":[13],"and":[17,36,55,68,82,130,161,168,179,186,198],"immune":[18],"system.":[19],"Typically,":[20],"pathologists":[21],"use":[22],"microscope":[23],"for":[24,62,183],"manual":[26],"inspection":[27],"smears":[30],"which":[31],"is":[32,74,87,115],"a":[33,49,75,95,121],"time-consuming,":[34],"error-prone,":[35],"labor-intensive":[37],"procedure.":[38],"To":[39],"address":[40],"these":[41],"issues,":[42],"we":[43],"present":[44],"two":[45],"novel":[46,96],"shallow":[47,76],"networks:":[48],"leukocyte":[50,56],"deep":[51,57,108,118],"segmentation":[52,59,65,147],"network":[53,60],"(LDS-Net)":[54],"aggregation":[58],"(LDAS-Net)":[61],"joint":[64,132],"cytoplasm":[67,129],"nuclei":[69,131,172],"in":[70,120,148],"WBC":[71,141],"images.":[72],"LDS-Net":[73,92],"architecture":[77],"with":[78,117,194],"three":[79],"downsampling":[80],"stages":[81],"seven":[83],"convolution":[84],"layers.":[85],"LDAS-Net":[86],"an":[88],"extended":[89],"version":[90],"that":[93],"utilizes":[94],"pool-less":[97],"low-level":[98,104],"information":[99,105,114],"transfer":[100,103],"bridge":[101],"to":[102,106,126],"layers":[109],"network.":[112],"This":[113],"aggregated":[116],"features":[119],"dense":[122],"feature":[123],"concatenation":[124],"block":[125],"achieve":[127],"accurate":[128],"segmentation.":[133],"We":[134],"evaluated":[135],"our":[136],"developed":[137],"architectures":[138],"on":[139,163],"four":[140],"publicly":[142],"available":[143],"datasets.":[144],"For":[145,171],"cytoplasmic":[146],"WBCs,":[149],"proposed":[151],"method":[152,190],"achieved":[153,182],"dice":[155,175],"coefficients":[156,176],"98.97%,":[158],"99.0%,":[159],"96.05%,":[160],"98.79%":[162],"Datasets":[164,184],"1,":[165],"2,":[166,187],"3,":[167],"4,":[169],"respectively.":[170,188],"segmentation,":[173],"96.35%":[178],"98.09%":[180],"1":[185],"Proposed":[189],"outperforms":[191],"state-of-the-art":[192],"methods":[193],"superior":[195],"computational":[196],"efficiency":[197],"requires":[199],"only":[200],"6.5":[201],"million":[202],"trainable":[203],"parameters.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}