{"id":"https://openalex.org/W3035647949","doi":"https://doi.org/10.1109/jstsp.2020.3001502","title":"BB-UNet: U-Net With Bounding Box Prior","display_name":"BB-UNet: U-Net With Bounding Box Prior","publication_year":2020,"publication_date":"2020-06-10","ids":{"openalex":"https://openalex.org/W3035647949","doi":"https://doi.org/10.1109/jstsp.2020.3001502","mag":"3035647949"},"language":"en","primary_location":{"id":"doi:10.1109/jstsp.2020.3001502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3001502","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://normandie-univ.hal.science/hal-02863197","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5013564362","display_name":"Rosana El Jurdi","orcid":"https://orcid.org/0000-0003-0509-9620"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"El Jurdi Rosana","raw_affiliation_strings":["Equipe Apprentissage"],"raw_orcid":"https://orcid.org/0000-0003-0509-9620","affiliations":[{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010409710","display_name":"Caroline Petitjean","orcid":"https://orcid.org/0000-0003-0013-5370"},"institutions":[{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]},{"id":"https://openalex.org/I88814501","display_name":"Institut National des Sciences Appliqu\u00e9es Rouen Normandie","ror":"https://ror.org/020ws7586","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I88814501"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Caroline Petitjean","raw_affiliation_strings":["UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, Normandie Universit\u00e9, Rouen, France","Equipe Apprentissage","Equipe Quantification en Imagerie Fonctionnelle"],"raw_orcid":"https://orcid.org/0000-0003-0013-5370","affiliations":[{"raw_affiliation_string":"UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, Normandie Universit\u00e9, Rouen, France","institution_ids":["https://openalex.org/I62396329","https://openalex.org/I88814501","https://openalex.org/I4210105918"]},{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]},{"raw_affiliation_string":"Equipe Quantification en Imagerie Fonctionnelle","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074267243","display_name":"Paul Honein\u00e9","orcid":"https://orcid.org/0000-0002-3042-183X"},"institutions":[{"id":"https://openalex.org/I4210105918","display_name":"Normandie Universit\u00e9","ror":"https://ror.org/01k40cz91","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918"]},{"id":"https://openalex.org/I62396329","display_name":"Universit\u00e9 de Rouen Normandie","ror":"https://ror.org/03nhjew95","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I62396329"]},{"id":"https://openalex.org/I88814501","display_name":"Institut National des Sciences Appliqu\u00e9es Rouen Normandie","ror":"https://ror.org/020ws7586","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210105918","https://openalex.org/I88814501"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Paul Honeine","raw_affiliation_strings":["UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, Normandie Universit\u00e9, Rouen, France","Equipe Apprentissage"],"raw_orcid":"https://orcid.org/0000-0002-3042-183X","affiliations":[{"raw_affiliation_string":"UNIROUEN, UNIHAVRE, INSA Rouen, LITIS, Normandie Universit\u00e9, Rouen, France","institution_ids":["https://openalex.org/I62396329","https://openalex.org/I88814501","https://openalex.org/I4210105918"]},{"raw_affiliation_string":"Equipe Apprentissage","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047285187","display_name":"Fahed Abdallah","orcid":"https://orcid.org/0000-0002-9485-6787"},"institutions":[{"id":"https://openalex.org/I140494188","display_name":"Universit\u00e9 de Technologie de Troyes","ror":"https://ror.org/01qhqcj41","country_code":"FR","type":"education","lineage":["https://openalex.org/I140494188"]},{"id":"https://openalex.org/I160368002","display_name":"Lebanese University","ror":"https://ror.org/05x6qnc69","country_code":"LB","type":"education","lineage":["https://openalex.org/I160368002"]}],"countries":["FR","LB"],"is_corresponding":false,"raw_author_name":"Fahed Abdallah","raw_affiliation_strings":["Lebanese University, Beirut, Lebanon","University of Technology of Troyes, Troyes, France","\u0627\u0644\u062c\u0627\u0645\u0639\u0629 \u0627\u0644\u0644\u0628\u0646\u0627\u0646\u064a\u0629 [\u0628\u064a\u0631\u0648\u062a] = Lebanese University [Beirut] = Universit\u00e9 libanaise [Beyrouth]","Laboratoire Mod\u00e9lisation et S\u00fbret\u00e9 des Syst\u00e8mes"],"raw_orcid":"https://orcid.org/0000-0002-9485-6787","affiliations":[{"raw_affiliation_string":"Lebanese University, Beirut, Lebanon","institution_ids":["https://openalex.org/I160368002"]},{"raw_affiliation_string":"University of Technology of Troyes, Troyes, France","institution_ids":["https://openalex.org/I140494188"]},{"raw_affiliation_string":"\u0627\u0644\u062c\u0627\u0645\u0639\u0629 \u0627\u0644\u0644\u0628\u0646\u0627\u0646\u064a\u0629 [\u0628\u064a\u0631\u0648\u062a] = Lebanese University [Beirut] = Universit\u00e9 libanaise [Beyrouth]","institution_ids":["https://openalex.org/I160368002"]},{"raw_affiliation_string":"Laboratoire Mod\u00e9lisation et S\u00fbret\u00e9 des Syst\u00e8mes","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.4053,"has_fulltext":false,"cited_by_count":64,"citation_normalized_percentile":{"value":0.95669799,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"14","issue":"6","first_page":"1189","last_page":"1198"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9997000098228455,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9987999796867371,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9969000220298767,"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/minimum-bounding-box","display_name":"Minimum bounding box","score":0.8672138452529907},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7559386491775513},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7251572012901306},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6740812659263611},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6204304695129395},{"id":"https://openalex.org/keywords/bounding-overwatch","display_name":"Bounding overwatch","score":0.5751968026161194},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5276429057121277},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5210049152374268},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5115872025489807},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5071914196014404},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.48820334672927856},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4665951728820801},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42445671558380127},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4127737879753113},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4048357605934143},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36185330152511597},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.08516547083854675}],"concepts":[{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.8672138452529907},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7559386491775513},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7251572012901306},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6740812659263611},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6204304695129395},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.5751968026161194},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5276429057121277},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5210049152374268},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5115872025489807},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5071914196014404},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.48820334672927856},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4665951728820801},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42445671558380127},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4127737879753113},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4048357605934143},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36185330152511597},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.08516547083854675},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/jstsp.2020.3001502","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jstsp.2020.3001502","pdf_url":null,"source":{"id":"https://openalex.org/S42167783","display_name":"IEEE Journal of Selected Topics in Signal Processing","issn_l":"1932-4553","issn":["1932-4553","1941-0484"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:HAL:hal-02863197v1","is_oa":true,"landing_page_url":"https://normandie-univ.hal.science/hal-02863197","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing, 2020, 14 (6), pp.1189 - 1198. &#x27E8;10.1109/JSTSP.2020.3001502&#x27E9;","raw_type":"Journal articles"}],"best_oa_location":{"id":"pmh:oai:HAL:hal-02863197v1","is_oa":true,"landing_page_url":"https://normandie-univ.hal.science/hal-02863197","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Journal of Selected Topics in Signal Processing, 2020, 14 (6), pp.1189 - 1198. &#x27E8;10.1109/JSTSP.2020.3001502&#x27E9;","raw_type":"Journal articles"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.8100000023841858}],"awards":[{"id":"https://openalex.org/G1162980892","display_name":null,"funder_award_id":"ANR-18-CE23-0014","funder_id":"https://openalex.org/F4320320883","funder_display_name":"Agence Nationale de la Recherche"}],"funders":[{"id":"https://openalex.org/F4320320883","display_name":"Agence Nationale de la Recherche","ror":"https://ror.org/00rbzpz17"},{"id":"https://openalex.org/F4320329938","display_name":"R\u00e9gion Normandie","ror":null},{"id":"https://openalex.org/F4320335322","display_name":"European Regional Development Fund","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1495267108","https://openalex.org/W1783315696","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2101608218","https://openalex.org/W2124351162","https://openalex.org/W2133515615","https://openalex.org/W2168804568","https://openalex.org/W2221898772","https://openalex.org/W2523005792","https://openalex.org/W2526499595","https://openalex.org/W2552414813","https://openalex.org/W2615461745","https://openalex.org/W2620296437","https://openalex.org/W2738853914","https://openalex.org/W2753924563","https://openalex.org/W2767801289","https://openalex.org/W2782757030","https://openalex.org/W2783023427","https://openalex.org/W2798122215","https://openalex.org/W2798753173","https://openalex.org/W2830786177","https://openalex.org/W2890579034","https://openalex.org/W2891227135","https://openalex.org/W2900570535","https://openalex.org/W2900680440","https://openalex.org/W2903336507","https://openalex.org/W2911470197","https://openalex.org/W2914410118","https://openalex.org/W2915126261","https://openalex.org/W2927980542","https://openalex.org/W2941035604","https://openalex.org/W2955544543","https://openalex.org/W2962604251","https://openalex.org/W2962914239","https://openalex.org/W2963150697","https://openalex.org/W2963452667","https://openalex.org/W2963591054","https://openalex.org/W2972006294","https://openalex.org/W3100715827","https://openalex.org/W3102800025","https://openalex.org/W3106250896","https://openalex.org/W3113231417","https://openalex.org/W6639824700","https://openalex.org/W6727200977","https://openalex.org/W6727807870","https://openalex.org/W6744538013","https://openalex.org/W6745521783","https://openalex.org/W6747680804","https://openalex.org/W6750469568","https://openalex.org/W6751512325","https://openalex.org/W6753627452","https://openalex.org/W6759274242","https://openalex.org/W6761334669","https://openalex.org/W6766030010","https://openalex.org/W6771363005","https://openalex.org/W6785652829"],"related_works":["https://openalex.org/W4237171675","https://openalex.org/W3036286480","https://openalex.org/W4287027631","https://openalex.org/W3192357901","https://openalex.org/W2387360586","https://openalex.org/W2952736415","https://openalex.org/W3209723314","https://openalex.org/W3205398323","https://openalex.org/W2883297582","https://openalex.org/W4390524233"],"abstract_inverted_index":{"Medical":[0],"image":[1,39],"segmentation":[2,37,55,230,261],"is":[3,82,139,179,246],"the":[4,21,31,96,110,117,124,129,165,188,210,213,223,229,242,255,263,269,285],"process":[5],"of":[6,33,47,98,190,193,205,212,231,254,262],"anatomically":[7],"isolating":[8],"organs":[9,233],"for":[10,123],"analysis":[11],"and":[12,86,154,175,241,258,280],"treatment.":[13],"Leading":[14],"works":[15,28],"within":[16,141],"this":[17],"domain":[18],"emerged":[19],"with":[20],"well-known":[22],"U-Net.":[23],"Despite":[24],"its":[25],"success,":[26],"recent":[27],"have":[29],"shown":[30],"limitations":[32],"U-Net":[34,85],"to":[35,53,115,121,260],"conduct":[36],"given":[38,149,284],"particularities":[40],"such":[41,177],"as":[42,71,73,145,155,176,252],"noise,":[43],"corruption":[44],"or":[45],"lack":[46],"contrast.":[48],"Prior":[49],"knowledge":[50],"integration":[51],"allows":[52],"overcome":[54],"ambiguities.":[56],"This":[57],"paper":[58],"introduces":[59],"BB-UNet":[60],"(Bounding":[61],"Box":[62],"U-Net),":[63],"a":[64,90,146,150,160,182,194,202,247],"deep":[65],"learning":[66,278],"model":[67,77,81,118,138,148],"that":[68,268],"integrates":[69],"location":[70],"well":[72],"shape":[74],"prior":[75],"onto":[76,109],"training.":[78],"The":[79,101,136],"proposed":[80,102,137,214,270],"inspired":[83],"by":[84],"incorporates":[87],"priors":[88],"through":[89],"novel":[91],"convolutional":[92],"layer":[93],"introduced":[94],"at":[95,173,234],"level":[97],"skip":[99],"connections.":[100],"architecture":[103],"helps":[104],"in":[105,113,159,181,236,275],"presenting":[106],"attention":[107],"kernels":[108],"neural":[111],"training":[112],"order":[114],"guide":[116],"on":[119,133,201,219,228],"where":[120],"look":[122],"organs.":[125],"Moreover,":[126],"it":[127,199],"fine-tunes":[128],"encoder":[130],"layers":[131],"based":[132],"positional":[134],"constraints.":[135],"exploited":[140,180],"two":[142,220],"main":[143],"paradigms:":[144],"solo":[147],"fully":[151,195,276],"supervised":[152,162,277,287],"framework":[153],"an":[156],"ancillary":[157],"model,":[158,215],"weakly":[161,286],"setting.":[163],"In":[164],"current":[166],"experiments,":[167],"manual":[168],"bounding":[169],"boxes":[170],"are":[171,217],"fed":[172],"inference":[174],"BB-Unet":[178,186],"semi-automatic":[183],"setting;":[184],"however,":[185],"has":[187],"potential":[189],"being":[191],"part":[192,253],"automated":[196],"process,":[197],"if":[198],"relies":[200],"preliminary":[203],"step":[204],"object":[206],"detection.":[207],"To":[208],"validate":[209],"performance":[211],"experiments":[216],"conducted":[218],"public":[221],"datasets:":[222],"SegTHOR":[224],"dataset":[225,244,250],"which":[226,245],"focuses":[227],"thoracic":[232],"risk":[235],"computed":[237],"tomography":[238],"(CT)":[239],"images,":[240],"Cardiac":[243],"mono-modal":[248],"MRI":[249],"released":[251],"Decathlon":[256],"challenge":[257],"dedicated":[259],"left":[264],"atrium.":[265],"Results":[266],"show":[267],"method":[271],"outperforms":[272],"state-of-the-art":[273],"methods":[274],"frameworks":[279],"registers":[281],"relevant":[282],"results":[283],"domain.":[288]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":8},{"year":2023,"cited_by_count":13},{"year":2022,"cited_by_count":17},{"year":2021,"cited_by_count":12},{"year":2020,"cited_by_count":3}],"updated_date":"2026-06-20T22:02:38.213706","created_date":"2025-10-10T00:00:00"}
