{"id":"https://openalex.org/W4220967737","doi":"https://doi.org/10.1117/12.2606045","title":"Direct and indirect image rotation estimation methods of orthopedic x-ray images","display_name":"Direct and indirect image rotation estimation methods of orthopedic x-ray images","publication_year":2022,"publication_date":"2022-03-18","ids":{"openalex":"https://openalex.org/W4220967737","doi":"https://doi.org/10.1117/12.2606045"},"language":"en","primary_location":{"id":"doi:10.1117/12.2606045","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2606045","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":"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/A5060018462","display_name":"Holger Kunze","orcid":"https://orcid.org/0000-0002-7021-2370"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Holger Kunze","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062481511","display_name":"Florian Kordon","orcid":"https://orcid.org/0000-0003-1240-5809"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]},{"id":"https://openalex.org/I4210153902","display_name":"Siemens Healthineers (Germany)","ror":"https://ror.org/0449c4c15","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210153902"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Florian Kordon","raw_affiliation_strings":["Siemens Healthcare GmbH (Germany)","Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Siemens Healthcare GmbH (Germany)","institution_ids":["https://openalex.org/I4210153902"]},{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101619735","display_name":"Andreas Maier","orcid":"https://orcid.org/0000-0002-9550-5284"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Maier","raw_affiliation_strings":["Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025681380","display_name":"Katharina Breininger","orcid":"https://orcid.org/0000-0001-7600-5869"},"institutions":[{"id":"https://openalex.org/I181369854","display_name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg","ror":"https://ror.org/00f7hpc57","country_code":"DE","type":"education","lineage":["https://openalex.org/I181369854"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Katharina Breininger","raw_affiliation_strings":["Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Friedrich-Alexander-Univ. Erlangen-N\u00fcrnberg (Germany)","institution_ids":["https://openalex.org/I181369854"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3194,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.39088046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"178","issue":null,"first_page":"84","last_page":"84"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T14510","display_name":"Medical Imaging and Analysis","score":0.9994999766349792,"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/T10522","display_name":"Medical Imaging Techniques and Applications","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/T12422","display_name":"Radiomics and Machine Learning in Medical Imaging","score":0.9975000023841858,"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/rotation","display_name":"Rotation (mathematics)","score":0.78420090675354},{"id":"https://openalex.org/keywords/orientation","display_name":"Orientation (vector space)","score":0.7525421380996704},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6828957796096802},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6155329942703247},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5524659156799316},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5501846671104431},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.5264383554458618},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4572157859802246},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.43062663078308105},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34858691692352295},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32116323709487915},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.23743918538093567},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.07258328795433044}],"concepts":[{"id":"https://openalex.org/C74050887","wikidata":"https://www.wikidata.org/wiki/Q848368","display_name":"Rotation (mathematics)","level":2,"score":0.78420090675354},{"id":"https://openalex.org/C16345878","wikidata":"https://www.wikidata.org/wiki/Q107472979","display_name":"Orientation (vector space)","level":2,"score":0.7525421380996704},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6828957796096802},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6155329942703247},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5524659156799316},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5501846671104431},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.5264383554458618},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4572157859802246},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.43062663078308105},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34858691692352295},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32116323709487915},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.23743918538093567},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.07258328795433044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2606045","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2606045","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":[{"score":0.8999999761581421,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W5563761","https://openalex.org/W11178481","https://openalex.org/W1503346906","https://openalex.org/W2051493872","https://openalex.org/W2058961400","https://openalex.org/W2068852312","https://openalex.org/W2073689200","https://openalex.org/W2114652002","https://openalex.org/W2117539524","https://openalex.org/W2137682223","https://openalex.org/W2159498975","https://openalex.org/W2186551597","https://openalex.org/W2803124023","https://openalex.org/W2895219217","https://openalex.org/W2914298543","https://openalex.org/W2929424363","https://openalex.org/W3034975586","https://openalex.org/W3154236554","https://openalex.org/W6600222513","https://openalex.org/W6637050416","https://openalex.org/W6639824700","https://openalex.org/W6668969659","https://openalex.org/W6684191040","https://openalex.org/W6687483927","https://openalex.org/W6696909428","https://openalex.org/W6697925102","https://openalex.org/W6737819897","https://openalex.org/W6748853297","https://openalex.org/W6751554223","https://openalex.org/W6754893925","https://openalex.org/W6758188876","https://openalex.org/W6759313102","https://openalex.org/W6765869253","https://openalex.org/W6766724408","https://openalex.org/W6766978945","https://openalex.org/W6780416711","https://openalex.org/W6793987844"],"related_works":["https://openalex.org/W4379231730","https://openalex.org/W4389858081","https://openalex.org/W2501551404","https://openalex.org/W4385583601","https://openalex.org/W4298131179","https://openalex.org/W2113201962","https://openalex.org/W4395685956","https://openalex.org/W2799953226","https://openalex.org/W2244279624","https://openalex.org/W1522196789"],"abstract_inverted_index":{"For":[0,43,135],"many":[1,44],"medical":[2],"questions,":[3],"X-ray":[4,61,92,190],"imaging":[5],"belongs":[6],"to":[7,37,89,118,149,223,226],"the":[8,22,39,56,60,69,77,80,102,110,126,129,136,146,151,160,164,167,170,181,185,193,197,200,204,212,217,244],"gold":[9],"standard":[10,96],"for":[11,67,180,216,238],"diagnosis,":[12],"treatment":[13,15],"planning,":[14],"guidance,":[16],"and":[17,142],"surgery":[18],"assessment.":[19],"To":[20],"improve":[21],"reading":[23],"performance,":[24],"standardized":[25],"image":[26,41,186],"rotation":[27,115,187],"is":[28,53,107,122],"an":[29],"important":[30],"step.":[31],"We":[32,172],"propose":[33,86],"a":[34,73,87,99,119,178,229],"new":[35],"algorithm":[36],"estimate":[38],"correct":[40],"rotation.":[42],"body":[45],"regions,":[46],"one":[47],"line":[48,64,106],"can":[49,65,155,220],"be":[50,156,221],"defined":[51],"that":[52],"aligned":[54],"with":[55,94,177],"upright":[57],"orientation":[58,127,137],"of":[59,72,79,104,128,132,163,169,184,240,247],"image.":[62],"This":[63],"be,":[66],"example,":[68],"shaft":[70],"axis":[71,78,131],"long":[74],"bone":[75],"or":[76],"spine.":[81],"In":[82,98],"this":[83,105,133,248],"paper,":[84],"we":[85,139],"strategy":[88],"automatically":[90],"align":[91],"images":[93,191,239],"their":[95],"orientation.":[97],"first":[100],"step,":[101],"heatmap":[103],"determined":[108],"using":[109],"segmentation":[111],"network":[112],"D-LinkNet.":[113],"The":[114],"direction,":[116],"up":[117],"top-down":[120],"flip,":[121],"obtained":[123],"by":[124,208,228],"computing":[125],"main":[130],"heatmap.":[134],"computation,":[138],"compare":[140,173],"PCA":[141,147,233],"Hu":[143,153,209],"moments.":[144],"While":[145],"requires":[148],"threshold":[150],"heatmap,":[152],"moments":[154,210],"used":[157],"directly":[158],"on":[159,188],"output":[161],"values":[162],"network,":[165],"preserving":[166],"(un)certainty":[168],"segmentation.":[171],"these":[174],"two":[175],"methods":[176],"ResNet-18":[179],"direct":[182,230],"estimation":[183,219,231],"220":[189],"from":[192,235],"MURA":[194],"dataset":[195],"showing":[196],"wrist":[198],"in":[199],"AP":[201],"view.":[202],"With":[203],"heatmap-based":[205],"approach":[206],"followed":[207],"analysis,":[211],"median":[213],"absolute":[214],"error":[215],"angle":[218],"reduced":[222],"0.7\u00b0":[224],"compared":[225],"1.7\u00b0":[227],"method.":[232],"suffers":[234],"noisy":[236],"heatmaps":[237],"bad":[241],"quality":[242],"degrading":[243],"overall":[245],"performance":[246],"approach.":[249]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
