{"id":"https://openalex.org/W2549299049","doi":"https://doi.org/10.1109/ijcnn.2016.7727805","title":"Using Convolutional Neural Network for edge detection in musculoskeletal ultrasound images","display_name":"Using Convolutional Neural Network for edge detection in musculoskeletal ultrasound images","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2549299049","doi":"https://doi.org/10.1109/ijcnn.2016.7727805","mag":"2549299049"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2016.7727805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","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/A5010903344","display_name":"Shaima Ibraheem Jabbar","orcid":"https://orcid.org/0000-0001-5701-9341"},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Shaima I. Jabbar","raw_affiliation_strings":["Biomedical Engineering, Keele University, UK"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101628406","display_name":"Charles Day","orcid":"https://orcid.org/0000-0001-9707-9189"},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Charles R. Day","raw_affiliation_strings":["Computer Science, Keele University"],"affiliations":[{"raw_affiliation_string":"Computer Science, Keele University","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075332667","display_name":"Nicholas Heinz","orcid":null},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nicholas Heinz","raw_affiliation_strings":["MMedSci in Anatomical Sciences, Keele University, UK"],"affiliations":[{"raw_affiliation_string":"MMedSci in Anatomical Sciences, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034700384","display_name":"Edward K. Chadwick","orcid":"https://orcid.org/0000-0003-0877-5110"},"institutions":[{"id":"https://openalex.org/I56007636","display_name":"Keele University","ror":"https://ror.org/00340yn33","country_code":"GB","type":"education","lineage":["https://openalex.org/I56007636"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Edward K. Chadwick","raw_affiliation_strings":["Biomedical Engineering, Keele University, UK"],"affiliations":[{"raw_affiliation_string":"Biomedical Engineering, Keele University, UK","institution_ids":["https://openalex.org/I56007636"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5010903344"],"corresponding_institution_ids":["https://openalex.org/I56007636"],"apc_list":null,"apc_paid":null,"fwci":6.1841,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.9656178,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4619","last_page":"4626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10862","display_name":"AI in cancer detection","score":0.991100013256073,"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":0.991100013256073,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.989799976348877,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12874","display_name":"Digital Imaging for Blood Diseases","score":0.987500011920929,"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/ground-truth","display_name":"Ground truth","score":0.8661980628967285},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.8339685201644897},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7958746552467346},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7438857555389404},{"id":"https://openalex.org/keywords/canny-edge-detector","display_name":"Canny edge detector","score":0.7085888385772705},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6028851866722107},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5985016822814941},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5392159223556519},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5343517661094666},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.49327152967453003},{"id":"https://openalex.org/keywords/edge-detection","display_name":"Edge detection","score":0.48859816789627075},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4777316749095917},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.46112316846847534},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.29267722368240356}],"concepts":[{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.8661980628967285},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8339685201644897},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7958746552467346},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7438857555389404},{"id":"https://openalex.org/C14705441","wikidata":"https://www.wikidata.org/wiki/Q597183","display_name":"Canny edge detector","level":5,"score":0.7085888385772705},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6028851866722107},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5985016822814941},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5392159223556519},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5343517661094666},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.49327152967453003},{"id":"https://openalex.org/C193536780","wikidata":"https://www.wikidata.org/wiki/Q1513153","display_name":"Edge detection","level":4,"score":0.48859816789627075},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4777316749095917},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.46112316846847534},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.29267722368240356}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2016.7727805","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2016.7727805","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G5997868131","display_name":null,"funder_award_id":"Project","funder_id":"https://openalex.org/F4320321079","funder_display_name":"Russian Foundation for Basic Research"}],"funders":[{"id":"https://openalex.org/F4320321079","display_name":"Russian Foundation for Basic Research","ror":"https://ror.org/02mh1ke95"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W22040386","https://openalex.org/W264023076","https://openalex.org/W1544758928","https://openalex.org/W1996070955","https://openalex.org/W2084546754","https://openalex.org/W2102096878","https://openalex.org/W2103061399","https://openalex.org/W2109553965","https://openalex.org/W2112796928","https://openalex.org/W2129967199","https://openalex.org/W2133196015","https://openalex.org/W2135676645","https://openalex.org/W2145023731","https://openalex.org/W2163605009","https://openalex.org/W2167510172","https://openalex.org/W2329786800","https://openalex.org/W2404019942","https://openalex.org/W2551883569","https://openalex.org/W4235289105","https://openalex.org/W4285719527","https://openalex.org/W6600893292","https://openalex.org/W6684191040","https://openalex.org/W6684372118"],"related_works":["https://openalex.org/W2549299049","https://openalex.org/W2340360913","https://openalex.org/W2763966779","https://openalex.org/W4368362586","https://openalex.org/W2125519506","https://openalex.org/W158826679","https://openalex.org/W1981132553","https://openalex.org/W3146545704","https://openalex.org/W2118331541","https://openalex.org/W2065071682"],"abstract_inverted_index":{"Fast":[0],"and":[1,214,240],"accurate":[2],"segmentation":[3],"of":[4,22,47,56,59,67,83,97,108,181],"musculoskeletal":[5,41,98],"ultrasound":[6,99,202],"images":[7],"is":[8,90,124,228,238],"an":[9,54,72,125,144,167],"on-going":[10],"challenge.":[11],"Two":[12],"principal":[13],"factors":[14],"make":[15],"this":[16,171],"task":[17],"difficult:":[18],"firstly,":[19],"the":[20,27,36,48,57,95,106,151,155,162,174,182,189,242,246],"presence":[21],"speckle":[23],"noise":[24],"arising":[25],"from":[26,185],"interference":[28],"that":[29,43,75,119,219],"accompanies":[30],"all":[31],"coherent":[32],"imaging":[33,86],"approaches;":[34],"secondly,":[35],"sometimes":[37],"subtle":[38],"interaction":[39],"between":[40],"components":[42],"leads":[44],"to":[45,64,112,149,244],"non-uniformity":[46],"image":[49,81,164,227,247],"intensity.":[50],"Our":[51,130,236],"work":[52,103],"presents":[53],"investigation":[55],"potential":[58,243],"Convolutional":[60],"Neural":[61],"Networks":[62],"(CNNs)":[63],"address":[65],"both":[66],"these":[68],"problems.":[69],"CNNs":[70,109,131,175],"are":[71,132],"effective":[73],"tool":[74],"has":[76,241],"previously":[77],"been":[78,177],"used":[79],"in":[80],"processing":[82,96,248],"several":[84],"biomedical":[85],"modalities.":[87],"However,":[88],"there":[89],"little":[91],"published":[92],"material":[93],"addressing":[94],"images.":[100,203],"In":[101,170],"our":[102],"we":[104],"explore":[105],"effectiveness":[107],"when":[110,222],"trained":[111,133,178,252],"act":[113],"as":[114],"a":[115,122],"pre-segmentation":[116],"pixel":[117,123],"classifier":[118],"determines":[120],"whether":[121],"edge":[126,147],"or":[127],"non-edge":[128],"pixel.":[129],"using":[134,161,179,188,199,208,223,231,250],"two":[135],"different":[136],"ground":[137,152,157,225,233],"truth":[138,153,158,226,234],"interpretations.":[139],"The":[140,216],"first":[141],"one":[142,186],"uses":[143],"automatic":[145],"Canny":[146,232],"detector":[148],"provide":[150],"image;":[154],"second":[156],"was":[159,195,206],"obtained":[160],"same":[163],"marked-up":[165],"by":[166],"expert":[168,224],"anatomist.":[169],"initial":[172],"study":[173],"have":[176],"half":[180,191],"prepared":[183],"data":[184],"image,":[187],"other":[190],"for":[192],"testing;":[193],"validation":[194],"also":[196],"carried":[197],"out":[198],"three":[200],"unseen":[201],"CNN":[204,220],"performance":[205,221],"assessed":[207],"Mathew's":[209],"Correlation":[210],"Coefficient,":[211],"Sensitivity,":[212],"Specificity":[213],"Accuracy.":[215],"results":[217],"show":[218],"better":[229],"than":[230],"image.":[235],"technique":[237],"promising":[239],"speed-up":[245],"pipeline":[249],"appropriately":[251],"CNNs.":[253]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":2}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}