{"id":"https://openalex.org/W2954762082","doi":"https://doi.org/10.1145/3330393.3330405","title":"An Automatic Detection Scheme of Acute Stanford Type A Aortic Dissection Based on DCNNs in CTA Images","display_name":"An Automatic Detection Scheme of Acute Stanford Type A Aortic Dissection Based on DCNNs in CTA Images","publication_year":2019,"publication_date":"2019-05-10","ids":{"openalex":"https://openalex.org/W2954762082","doi":"https://doi.org/10.1145/3330393.3330405","mag":"2954762082"},"language":"en","primary_location":{"id":"doi:10.1145/3330393.3330405","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3330393.3330405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal Processing","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/A5100699602","display_name":"Xin Xu","orcid":"https://orcid.org/0000-0002-4383-8300"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xin Xu","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100689912","display_name":"Zhiqiang He","orcid":"https://orcid.org/0000-0002-5010-3118"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiqiang He","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing, China, Guizhou Provincial Engineering Research Center for Livelihood Big Data Comprehensive Application"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing, China, Guizhou Provincial Engineering Research Center for Livelihood Big Data Comprehensive Application","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008455605","display_name":"Kai Niu","orcid":"https://orcid.org/0000-0002-8076-1867"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Niu","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100610198","display_name":"Yijie Zhang","orcid":"https://orcid.org/0000-0003-1594-1789"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yijie Zhang","raw_affiliation_strings":["Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100662197","display_name":"Hao Tang","orcid":"https://orcid.org/0000-0003-2202-0515"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]},{"id":"https://openalex.org/I4210159865","display_name":"Xiangya Hospital Central South University","ror":"https://ror.org/05c1yfj14","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210159865"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Tang","raw_affiliation_strings":["Department of Cardiovascular Surgery, The Second, Xiangya Hospital, Central South University, Changsha, Hunan, China"],"affiliations":[{"raw_affiliation_string":"Department of Cardiovascular Surgery, The Second, Xiangya Hospital, Central South University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I4210159865","https://openalex.org/I139660479"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101527634","display_name":"Ling Tan","orcid":"https://orcid.org/0000-0002-8779-0169"},"institutions":[{"id":"https://openalex.org/I139660479","display_name":"Central South University","ror":"https://ror.org/00f1zfq44","country_code":"CN","type":"education","lineage":["https://openalex.org/I139660479"]},{"id":"https://openalex.org/I4210159865","display_name":"Xiangya Hospital Central South University","ror":"https://ror.org/05c1yfj14","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210159865"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ling Tan","raw_affiliation_strings":["Department of Cardiovascular Surgery, The Second, Xiangya Hospital, Central South University, Changsha, Hunan, China"],"affiliations":[{"raw_affiliation_string":"Department of Cardiovascular Surgery, The Second, Xiangya Hospital, Central South University, Changsha, Hunan, China","institution_ids":["https://openalex.org/I4210159865","https://openalex.org/I139660479"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100699602"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.8098,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.76859306,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"16","last_page":"20"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.995199978351593,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.995199978351593,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9835000038146973,"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/T10983","display_name":"Aortic aneurysm repair treatments","score":0.9761999845504761,"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/aortic-dissection","display_name":"Aortic dissection","score":0.7897340655326843},{"id":"https://openalex.org/keywords/descending-aorta","display_name":"Descending aorta","score":0.5740970373153687},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5551730990409851},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.5510643720626831},{"id":"https://openalex.org/keywords/aorta","display_name":"Aorta","score":0.5498162508010864},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5162820219993591},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5085865259170532},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.48654285073280334},{"id":"https://openalex.org/keywords/radiology","display_name":"Radiology","score":0.4831232726573944},{"id":"https://openalex.org/keywords/dissection","display_name":"Dissection (medical)","score":0.45936957001686096},{"id":"https://openalex.org/keywords/ascending-aorta","display_name":"Ascending aorta","score":0.442402184009552},{"id":"https://openalex.org/keywords/angiography","display_name":"Angiography","score":0.4298841655254364},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4288642406463623},{"id":"https://openalex.org/keywords/aortic-arch","display_name":"Aortic arch","score":0.42246925830841064},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40794801712036133},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.19264835119247437}],"concepts":[{"id":"https://openalex.org/C2779993142","wikidata":"https://www.wikidata.org/wiki/Q594660","display_name":"Aortic dissection","level":3,"score":0.7897340655326843},{"id":"https://openalex.org/C2780663194","wikidata":"https://www.wikidata.org/wiki/Q3270170","display_name":"Descending aorta","level":3,"score":0.5740970373153687},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5551730990409851},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5510643720626831},{"id":"https://openalex.org/C2779980429","wikidata":"https://www.wikidata.org/wiki/Q101004","display_name":"Aorta","level":2,"score":0.5498162508010864},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5162820219993591},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5085865259170532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.48654285073280334},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.4831232726573944},{"id":"https://openalex.org/C2775862295","wikidata":"https://www.wikidata.org/wiki/Q2660911","display_name":"Dissection (medical)","level":2,"score":0.45936957001686096},{"id":"https://openalex.org/C2778249958","wikidata":"https://www.wikidata.org/wiki/Q2349469","display_name":"Ascending aorta","level":3,"score":0.442402184009552},{"id":"https://openalex.org/C2780643987","wikidata":"https://www.wikidata.org/wiki/Q468414","display_name":"Angiography","level":2,"score":0.4298841655254364},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4288642406463623},{"id":"https://openalex.org/C2781285907","wikidata":"https://www.wikidata.org/wiki/Q616032","display_name":"Aortic arch","level":3,"score":0.42246925830841064},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40794801712036133},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.19264835119247437}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3330393.3330405","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3330393.3330405","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 4th International Conference on Multimedia Systems and Signal 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":12,"referenced_works":["https://openalex.org/W2072081124","https://openalex.org/W2085667661","https://openalex.org/W2114509515","https://openalex.org/W2145023731","https://openalex.org/W2194775991","https://openalex.org/W2436576671","https://openalex.org/W2608014924","https://openalex.org/W2706946672","https://openalex.org/W2735351371","https://openalex.org/W2963150697","https://openalex.org/W6687483927","https://openalex.org/W6735463952"],"related_works":["https://openalex.org/W3028960828","https://openalex.org/W4210662164","https://openalex.org/W2201447998","https://openalex.org/W1972304518","https://openalex.org/W2889722562","https://openalex.org/W2407626330","https://openalex.org/W2101159284","https://openalex.org/W2945022013","https://openalex.org/W2324676631","https://openalex.org/W3134827307"],"abstract_inverted_index":{"As":[0],"a":[1,36,101],"severely":[2],"life-threatening":[3],"disease,":[4],"acute":[5],"Stanford":[6],"type":[7],"A":[8],"aortic":[9,29,45,132,149],"dissection":[10,46,133],"brings":[11],"patients":[12],"with":[13],"high":[14],"potential":[15],"of":[16,28,44,86,103,156,167],"death.":[17],"Computed":[18],"Tomography":[19],"Angiography":[20],"(CTA)":[21],"images":[22,113,130],"become":[23],"the":[24,58,77,80,110,116,141,148,154],"effective":[25],"diagnostic":[26],"basis":[27],"dissection.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34,92,108,136],"propose":[35],"scheme":[37],"for":[38,131],"automatic":[39],"patient-level":[40,174],"and":[41,62,65,75,143,176],"site-level":[42,179],"detection":[43,175],"based":[47],"on":[48,60,160],"Deep":[49],"Convolutional":[50],"Neural":[51],"Networks":[52],"(DCNNs)":[53],"in":[54,127,169,173,178],"order":[55],"to":[56,72,99,114],"relieve":[57],"burden":[59],"doctors":[61],"diagnose":[63],"accurately":[64],"timely.":[66],"Firstly,":[67],"Mask":[68],"R-CNN":[69],"is":[70],"adopted":[71],"precisely":[73],"detect":[74,138],"segment":[76],"aorta":[78,112,129],"from":[79],"original":[81],"CTA":[82,90],"images.":[83],"Taking":[84],"advantage":[85],"spatial":[87],"continuity":[88],"among":[89],"images,":[91,163],"put":[93],"forward":[94],"an":[95,165],"area-wise":[96],"screening":[97],"method":[98],"remove":[100],"handful":[102],"unsatisfactory":[104],"segmentation":[105],"results.":[106],"Then":[107],"process":[109],"segmented":[111],"extract":[115],"edges":[117],"by":[118],"Canny":[119],"edge":[120],"detector.":[121],"Finally,":[122],"two":[123],"ResNets":[124],"are":[125],"employed":[126],"processed":[128],"detection.":[134,180],"Specially,":[135],"can":[137],"not":[139],"only":[140],"ascending":[142],"descending":[144],"aorta,":[145],"but":[146],"also":[147],"arch,":[150],"which":[151],"may":[152],"be":[153],"site":[155],"onset.":[157],"We":[158],"tested":[159],"over":[161],"3500":[162],"achieving":[164],"accuracy":[166],"98.66%":[168],"slice-level":[170],"detection,":[171],"96.51%":[172],"91.47%":[177]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}