{"id":"https://openalex.org/W4213001306","doi":"https://doi.org/10.1117/12.2611437","title":"Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors","display_name":"Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors","publication_year":2022,"publication_date":"2022-02-18","ids":{"openalex":"https://openalex.org/W4213001306","doi":"https://doi.org/10.1117/12.2611437"},"language":"en","primary_location":{"id":"doi:10.1117/12.2611437","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2611437","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/A5047289228","display_name":"Sarah D. Verboom","orcid":"https://orcid.org/0009-0004-9757-9422"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Sarah D. Verboom","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018325225","display_name":"Marco Caballo","orcid":"https://orcid.org/0000-0002-3700-2785"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Marco Caballo","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061073014","display_name":"Mireille J. M. Broeders","orcid":"https://orcid.org/0000-0002-8741-8148"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Mireille J. M. Broeders","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031023373","display_name":"Jonas Teuwen","orcid":"https://orcid.org/0000-0002-1825-1428"},"institutions":[{"id":"https://openalex.org/I2898336195","display_name":"The Netherlands Cancer Institute","ror":"https://ror.org/03xqtf034","country_code":"NL","type":"healthcare","lineage":["https://openalex.org/I2898336195"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Jonas Teuwen","raw_affiliation_strings":["The Netherlands Cancer Institute  (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Netherlands Cancer Institute  (Netherlands)","institution_ids":["https://openalex.org/I2898336195"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5079270012","display_name":"Ioannis Sechopoulos","orcid":"https://orcid.org/0000-0001-9615-8205"},"institutions":[{"id":"https://openalex.org/I145872427","display_name":"Radboud University Nijmegen","ror":"https://ror.org/016xsfp80","country_code":"NL","type":"education","lineage":["https://openalex.org/I145872427"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Ioannis Sechopoulos","raw_affiliation_strings":["Radboud Univ. Medical Ctr. (Netherlands)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Radboud Univ. Medical Ctr. (Netherlands)","institution_ids":["https://openalex.org/I145872427"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.1687,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.36063071,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"73","issue":null,"first_page":"111","last_page":"111"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9998000264167786,"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":0.9998000264167786,"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/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.9973000288009644,"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/segmentation","display_name":"Segmentation","score":0.8482493162155151},{"id":"https://openalex.org/keywords/pectoral-muscle","display_name":"Pectoral muscle","score":0.7409453392028809},{"id":"https://openalex.org/keywords/s\u00f8rensen\u2013dice-coefficient","display_name":"S\u00f8rensen\u2013Dice coefficient","score":0.7261219024658203},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.654338002204895},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5456094145774841},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.5260182023048401},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.44342342019081116},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.40684449672698975},{"id":"https://openalex.org/keywords/anatomy","display_name":"Anatomy","score":0.3478066325187683},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.17420974373817444},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.10134604573249817}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8482493162155151},{"id":"https://openalex.org/C2909720742","wikidata":"https://www.wikidata.org/wiki/Q660627","display_name":"Pectoral muscle","level":2,"score":0.7409453392028809},{"id":"https://openalex.org/C163892561","wikidata":"https://www.wikidata.org/wiki/Q2613728","display_name":"S\u00f8rensen\u2013Dice coefficient","level":4,"score":0.7261219024658203},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.654338002204895},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5456094145774841},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.5260182023048401},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.44342342019081116},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.40684449672698975},{"id":"https://openalex.org/C105702510","wikidata":"https://www.wikidata.org/wiki/Q514","display_name":"Anatomy","level":1,"score":0.3478066325187683},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.17420974373817444},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.10134604573249817},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.0},{"id":"https://openalex.org/C121608353","wikidata":"https://www.wikidata.org/wiki/Q12078","display_name":"Cancer","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2611437","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2611437","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1550497686","https://openalex.org/W2127890285","https://openalex.org/W2528323936","https://openalex.org/W2889985731","https://openalex.org/W2952865381","https://openalex.org/W3028293673","https://openalex.org/W3044184276","https://openalex.org/W3098585799","https://openalex.org/W3099319035","https://openalex.org/W3110148606","https://openalex.org/W3198256623","https://openalex.org/W4226512252","https://openalex.org/W4235573787","https://openalex.org/W4247836216","https://openalex.org/W4250685322","https://openalex.org/W4362597616","https://openalex.org/W6639824700","https://openalex.org/W6801235890"],"related_works":["https://openalex.org/W3197954266","https://openalex.org/W4389009345","https://openalex.org/W3047746737","https://openalex.org/W3021454079","https://openalex.org/W1522196789","https://openalex.org/W4287691568","https://openalex.org/W4220718606","https://openalex.org/W2085143385","https://openalex.org/W2005489729","https://openalex.org/W3090294949"],"abstract_inverted_index":{"Segmentation":[0],"of":[1,46,51,66,110,195,234,247],"digital":[2],"mammograms":[3,243],"(DMs)":[4],"into":[5],"background,":[6,153],"breast,":[7,154],"and":[8,38,43,61,70,90,126,129,155,167,236],"pectoral":[9,75,156,171,196,206],"muscle":[10,172,197],"is":[11,23],"an":[12],"important":[13],"pre-processing":[14],"step":[15],"for":[16,30,135,152,180,220],"many":[17],"medical":[18],"imaging":[19],"pipelines.":[20],"Our":[21],"aim":[22],"to":[24,117,239],"propose":[25],"a":[26,74,99,103,176],"segmentation":[27,95,160,173,198,231],"method":[28,227],"suited":[29],"processed":[31,64],"DMs":[32],"that":[33],"generalizes":[34],"across":[35,44,119],"cranio-caudal":[36],"(CC)":[37],"medio-lateral":[39],"oblique":[40],"(MLO)":[41],"projections,":[42],"models":[45,86],"different":[47,84,209,222,248],"vendors.":[48,249],"A":[49],"dataset":[50],"247":[52],"diagnostic":[53],"DM":[54,85],"exams":[55],"was":[56,96,199,237],"used,":[57],"totaling":[58],"493":[59],"CC":[60,166,186,202],"494":[62],"MLO":[63,168,181],"images,":[65],"which":[67],"199":[68],"(40.4%)":[69],"486":[71],"(98.4%)":[72],"contained":[73],"muscle,":[76,157],"respectively.":[77,158],"The":[78,93,132,170,191,225],"images":[79,187,203],"were":[80,113,138],"acquired":[81,244],"with":[82,102],"10":[83],"from":[87,218],"GE":[88],"(73%)":[89],"Siemens":[91],"(27%).":[92],"multi-class":[94,104],"done":[97],"by":[98,245],"U-Net":[100],"trained":[101],"weighted":[105],"focal":[106],"loss.":[107],"Several":[108],"types":[109],"data":[111],"augmentation":[112],"used":[114],"during":[115],"training,":[116],"generalize":[118,240],"model":[120,210],"types,":[121,211],"including":[122],"random":[123,127],"look-up":[124],"table":[125],"elastic":[128],"gamma":[130],"transformations.":[131],"DICE":[133,178,215],"coefficients":[134,216],"the":[136,212,221],"segmentations":[137],"(mean":[139],"\u00b1":[140,144,147,150,183,189],"std.":[141],"dev.)":[142],"0.995":[143],"0.005,":[145],"0.980":[146],"0.016,":[148],"0.839":[149],"0.243":[151],"Background":[159],"did":[161],"not":[162],"differ":[163],"significantly":[164],"between":[165],"images.":[169],"resulted":[174],"in":[175,201],"higher":[177],"coefficient":[179],"(0.932":[182],"0.104)":[184],"than":[185],"(0.636":[188],"0.323).":[190],"false":[192],"positive":[193],"rate":[194],"1.5%":[200],"without":[204],"any":[205],"muscle.":[207],"Among":[208],"mean":[213],"overall":[214,230],"ranged":[217],"0.985-0.990":[219],"system":[223],"models.":[224],"developed":[226],"yielded":[228],"accurate":[229],"results,":[232],"independent":[233],"view,":[235],"able":[238],"well":[241],"over":[242],"systems":[246]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
