{"id":"https://openalex.org/W3000729558","doi":"https://doi.org/10.1109/ivcnz48456.2019.8960956","title":"Automated Segmentation of Breast Arterial Calcifications from Digital Mammography","display_name":"Automated Segmentation of Breast Arterial Calcifications from Digital Mammography","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3000729558","doi":"https://doi.org/10.1109/ivcnz48456.2019.8960956","mag":"3000729558"},"language":"en","primary_location":{"id":"doi:10.1109/ivcnz48456.2019.8960956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz48456.2019.8960956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","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/A5059763093","display_name":"Kaier Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097193","display_name":"Technologies pour la Sant\u00e9","ror":"https://ror.org/012hpz460","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488","https://openalex.org/I4210097193"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Kaier Wang","raw_affiliation_strings":["Volpara Health Technologies, Wellington"],"affiliations":[{"raw_affiliation_string":"Volpara Health Technologies, Wellington","institution_ids":["https://openalex.org/I4210097193"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067487424","display_name":"Nabeel Khan","orcid":"https://orcid.org/0000-0002-2873-4554"},"institutions":[{"id":"https://openalex.org/I4210097193","display_name":"Technologies pour la Sant\u00e9","ror":"https://ror.org/012hpz460","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488","https://openalex.org/I4210097193"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Nabeel Khan","raw_affiliation_strings":["Volpara Health Technologies, Wellington"],"affiliations":[{"raw_affiliation_string":"Volpara Health Technologies, Wellington","institution_ids":["https://openalex.org/I4210097193"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067717824","display_name":"Ralph Highnam","orcid":null},"institutions":[{"id":"https://openalex.org/I4210097193","display_name":"Technologies pour la Sant\u00e9","ror":"https://ror.org/012hpz460","country_code":"FR","type":"government","lineage":["https://openalex.org/I154526488","https://openalex.org/I4210097193"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Ralph Highnam","raw_affiliation_strings":["Volpara Health Technologies, Wellington"],"affiliations":[{"raw_affiliation_string":"Volpara Health Technologies, Wellington","institution_ids":["https://openalex.org/I4210097193"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059763093"],"corresponding_institution_ids":["https://openalex.org/I4210097193"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.18282005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.9991999864578247,"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":0.9991999864578247,"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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9926999807357788,"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"}},{"id":"https://openalex.org/T11361","display_name":"Digital Radiography and Breast Imaging","score":0.9922000169754028,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/thresholding","display_name":"Thresholding","score":0.6964526772499084},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6751062870025635},{"id":"https://openalex.org/keywords/hessian-matrix","display_name":"Hessian matrix","score":0.6722198724746704},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6426858901977539},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6371383666992188},{"id":"https://openalex.org/keywords/digital-mammography","display_name":"Digital mammography","score":0.5941461324691772},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5382620096206665},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.5222076773643494},{"id":"https://openalex.org/keywords/mammography","display_name":"Mammography","score":0.4769143760204315},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4743455946445465},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.46668633818626404},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41499078273773193},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4125326871871948},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.20893076062202454},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15475794672966003},{"id":"https://openalex.org/keywords/breast-cancer","display_name":"Breast cancer","score":0.13093435764312744},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11594784259796143},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.09534624218940735}],"concepts":[{"id":"https://openalex.org/C191178318","wikidata":"https://www.wikidata.org/wiki/Q2256906","display_name":"Thresholding","level":3,"score":0.6964526772499084},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6751062870025635},{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.6722198724746704},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6426858901977539},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6371383666992188},{"id":"https://openalex.org/C2781281974","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Digital mammography","level":5,"score":0.5941461324691772},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5382620096206665},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.5222076773643494},{"id":"https://openalex.org/C2780472235","wikidata":"https://www.wikidata.org/wiki/Q324634","display_name":"Mammography","level":4,"score":0.4769143760204315},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4743455946445465},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.46668633818626404},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41499078273773193},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4125326871871948},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.20893076062202454},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15475794672966003},{"id":"https://openalex.org/C530470458","wikidata":"https://www.wikidata.org/wiki/Q128581","display_name":"Breast cancer","level":3,"score":0.13093435764312744},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11594784259796143},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.09534624218940735},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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},{"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/C28826006","wikidata":"https://www.wikidata.org/wiki/Q33521","display_name":"Applied mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivcnz48456.2019.8960956","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivcnz48456.2019.8960956","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Conference on Image and Vision Computing New Zealand (IVCNZ)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5400000214576721,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W948663339","https://openalex.org/W1901129140","https://openalex.org/W1947673900","https://openalex.org/W1968093678","https://openalex.org/W1985414551","https://openalex.org/W2076063813","https://openalex.org/W2094213695","https://openalex.org/W2098699875","https://openalex.org/W2101608218","https://openalex.org/W2122436376","https://openalex.org/W2129534965","https://openalex.org/W2138675231","https://openalex.org/W2147277215","https://openalex.org/W2167510172","https://openalex.org/W2194775991","https://openalex.org/W2274668089","https://openalex.org/W2301766697","https://openalex.org/W2474421929","https://openalex.org/W2570343428","https://openalex.org/W2575657035","https://openalex.org/W2734349601","https://openalex.org/W2745067076","https://openalex.org/W2759356748","https://openalex.org/W2788865504","https://openalex.org/W2798999055","https://openalex.org/W2912581782","https://openalex.org/W2956378751","https://openalex.org/W2963037989","https://openalex.org/W2963163009","https://openalex.org/W2964189045","https://openalex.org/W2964309882","https://openalex.org/W6681640253","https://openalex.org/W6684372118","https://openalex.org/W6721136770","https://openalex.org/W6731892127"],"related_works":["https://openalex.org/W2557931800","https://openalex.org/W2116047071","https://openalex.org/W54977395","https://openalex.org/W1993531705","https://openalex.org/W3197491245","https://openalex.org/W1966034269","https://openalex.org/W2000845075","https://openalex.org/W1766776469","https://openalex.org/W2029515485","https://openalex.org/W1990413141"],"abstract_inverted_index":{"Breast":[0],"arterial":[1],"calcifications":[2],"(BACs)":[3],"are":[4,85],"formed":[5],"when":[6],"calcium":[7],"is":[8,23,63,74,153,160],"deposited":[9],"in":[10,15,53,88,114,155,164],"the":[11,16,39,78,98],"walls":[12],"of":[13,21,30,41,81,166],"arteries":[14],"breast.":[17],"The":[18,126],"accurate":[19],"segmentation":[20],"BACs":[22,52,67,84,111,156],"a":[24,34,58,70,92,135],"critical":[25],"step":[26],"for":[27,117,124],"risk":[28],"assessment":[29],"cardiovascular":[31],"disease":[32],"from":[33],"mammogram.":[35],"This":[36],"paper":[37],"evaluates":[38],"performance":[40],"three":[42],"deep":[43,119],"learning":[44,120],"architectures,":[45],"YOLO,":[46],"Unet":[47],"and":[48,122,141,158,170],"DeepLabv3+,":[49],"on":[50,138],"detecting":[51],"digital":[54,106],"mammography.":[55],"In":[56,102],"comparison,":[57],"simple":[59,150],"Hessian-based":[60,131],"multiscale":[61],"filter":[62],"developed":[64,91],"to":[65,76,95],"enhance":[66],"pattern,":[68],"then":[69],"self-adaptive":[71],"thresholding":[72],"algorithm":[73],"applied":[75],"obtain":[77],"binary":[79],"mask":[80],"BACs.":[82],"As":[83],"relatively":[86],"small":[87,99],"size,":[89],"we":[90],"new":[93],"metric":[94],"better":[96],"evaluate":[97],"object":[100],"segmentation.":[101],"this":[103],"study,":[104],"135":[105],"mammographic":[107],"images":[108],"containing":[109],"labelled":[110],"were":[112],"obtained,":[113],"which":[115],"80%":[116],"training":[118],"networks":[121],"20%":[123],"validation.":[125],"results":[127],"show":[128],"that":[129],"our":[130],"filtering":[132,151],"method":[133],"achieves":[134],"highest":[136],"accuracy":[137],"validation":[139],"data,":[140],"DeepLabv3+":[142,159],"falls":[143],"behind":[144],"with":[145],"little":[146],"effectiveness.":[147],"We":[148],"conclude":[149],"technique":[152],"effective":[154],"extraction,":[157],"an":[161],"expensive":[162],"alternative":[163],"terms":[165],"its":[167],"computational":[168],"cost":[169],"configuration":[171],"complexity.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}