{"id":"https://openalex.org/W4220897400","doi":"https://doi.org/10.1117/12.2613295","title":"BAA-Net: attention-based CNN for automatic placental segmentation of MR images","display_name":"BAA-Net: attention-based CNN for automatic placental segmentation of MR images","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W4220897400","doi":"https://doi.org/10.1117/12.2613295"},"language":"en","primary_location":{"id":"doi:10.1117/12.2613295","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2613295","pdf_url":null,"source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: 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/A5100405560","display_name":"Jia Jia","orcid":"https://orcid.org/0000-0001-6395-9487"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Jia","raw_affiliation_strings":["Zhengzhou Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou Univ. (China)","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100455016","display_name":"Yaping Wang","orcid":"https://orcid.org/0000-0002-4956-6385"},"institutions":[{"id":"https://openalex.org/I38877650","display_name":"Zhengzhou University","ror":"https://ror.org/04ypx8c21","country_code":"CN","type":"education","lineage":["https://openalex.org/I38877650"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaping Wang","raw_affiliation_strings":["Zhengzhou Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhengzhou Univ. (China)","institution_ids":["https://openalex.org/I38877650"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102500545","display_name":"Chenyu Yan","orcid":"https://orcid.org/0009-0008-5498-6270"},"institutions":[{"id":"https://openalex.org/I4210156804","display_name":"First Affiliated Hospital of Zhengzhou University","ror":"https://ror.org/056swr059","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210156804"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenyu Yan","raw_affiliation_strings":["The First Affiliated Hospital of Zhengzhou Univ. (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The First Affiliated Hospital of Zhengzhou Univ. (China)","institution_ids":["https://openalex.org/I4210156804"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"110","last_page":"110"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.975600004196167,"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"}},"topics":[{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.975600004196167,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9007999897003174,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7240422964096069},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6983432769775391},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6781752109527588},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.642335832118988},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6168531179428101},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4526906907558441}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7240422964096069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6983432769775391},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6781752109527588},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.642335832118988},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6168531179428101},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4526906907558441}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2613295","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2613295","pdf_url":null,"source":{"id":"https://openalex.org/S4363607561","display_name":"Medical Imaging 2022: Image Processing","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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Medical Imaging 2022: Image Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.7400000095367432,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2058170566","https://openalex.org/W2772917594","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","https://openalex.org/W2775347418"],"abstract_inverted_index":{"The":[0,16,201,219],"placenta":[1,18,27,62],"is":[2,63,92,99,227,242],"an":[3],"important":[4,252],"organ":[5],"for":[6,130,198,229],"the":[7,11,14,34,38,58,67,75,95,122,128,137,146,154,162,173,177,184,187,199,224,230,236,245],"material":[8],"exchange":[9],"between":[10],"fetus":[12],"and":[13,28,40,66,98,109,148,203,215],"mother.":[15],"abnormal":[17],"may":[19],"lead":[20],"to":[21,127,135,157,175,244],"fetal":[22,48],"intrauterine":[23],"growth":[24],"restriction,":[25],"invasive":[26],"other":[29],"related":[30],"diseases,":[31],"thus":[32],"endangering":[33],"health":[35],"of":[36,44,61,119,161,186,192,247],"both":[37],"mother":[39],"fetus.":[41],"Accurate":[42,239],"segmentation":[43,60,71,87,139,155,241],"placental":[45,55,70,86,138,232,240,248],"tissue":[46],"in":[47,89],"magnetic":[49],"resonance":[50],"images":[51,191],"could":[52],"help":[53],"diagnose":[54],"abnormalities.":[56],"However,":[57],"manual":[59],"very":[64],"time-consuming,":[65],"semi":[68],"automatic":[69,85,231],"methods":[72],"still":[73],"require":[74],"operator\u2019s":[76],"interaction.":[77],"In":[78],"this":[79],"paper,":[80],"we":[81],"proposed":[82,188,225],"a":[83],"fully":[84],"method,":[88],"which":[90,250],"BiO-Net":[91],"used":[93,197],"as":[94,113],"backbone":[96],"network":[97],"further":[100],"improved":[101],"by":[102,210],"embedding":[103],"Atrous":[104],"Spatial":[105],"Pyramid":[106],"Pooling":[107],"(ASPP)":[108],"attention":[110,168],"mechanism,":[111],"termed":[112],"BAA-Net.":[114],"To":[115,182],"retain":[116],"more":[117],"details":[118],"boundary":[120],"information,":[121],"ASPP":[123],"module":[124],"was":[125],"introduced":[126,171],"encoder":[129,147],"capturing":[131],"high-resolution":[132],"feature":[133,143,180],"maps":[134],"improve":[136],"performance.":[140],"Because":[141],"different":[142,151],"channels":[144],"from":[145],"decoder":[149,174],"have":[150],"effects":[152],"on":[153],"task,":[156],"make":[158],"better":[159],"use":[160],"most":[163,178],"useful":[164],"features,":[165],"four":[166],"channel":[167],"modules":[169],"were":[170,196],"into":[172],"highlight":[176],"relevant":[179],"channels.":[181],"evaluate":[183],"performance":[185],"BAA-Net,":[189],"MR":[190],"20":[193],"pregnant":[194],"women":[195],"experiments.":[200],"Dice":[202],"average":[204],"symmetric":[205],"surface":[206],"distance":[207],"(ASSD)":[208],"obtained":[209],"our":[211],"BAA-Net":[212,226],"are":[213],"0.8674":[214],"2.8880":[216],"mm,":[217],"respectively.":[218],"experimental":[220],"results":[221],"show":[222],"that":[223],"effective":[228],"segmentation,":[233],"comparing":[234],"with":[235],"existing":[237],"methods.":[238],"conducive":[243],"diagnosis":[246],"abnormalities,":[249],"has":[251],"clinical":[253],"significance.":[254]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
