{"id":"https://openalex.org/W6908470866","doi":"https://doi.org/10.26083/tuprints-00020727","title":"Automatic model-based teeth segmentation, numbering and 3-D reconstruction using routinely collected images","display_name":"Automatic model-based teeth segmentation, numbering and 3-D reconstruction using routinely collected images","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W6908470866","doi":"https://doi.org/10.26083/tuprints-00020727"},"language":"en","primary_location":{"id":"pmh:oai:tubiblio.ulb.tu-darmstadt.de:131796","is_oa":false,"landing_page_url":"http://tubiblio.ulb.tu-darmstadt.de/131796/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196390","display_name":"TUbilio (Technical University of Darmstadt)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31512782","host_organization_name":"Technische Universit\u00e4t Darmstadt","host_organization_lineage":["https://openalex.org/I31512782"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Wirtz, Andreas","orcid":"https://orcid.org/0000-0003-3108-3550"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wirtz, Andreas","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3108-3550","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.5375999808311462,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11363","display_name":"Dental Radiography and Imaging","score":0.5375999808311462,"subfield":{"id":"https://openalex.org/subfields/3504","display_name":"Oral Surgery"},"field":{"id":"https://openalex.org/fields/35","display_name":"Dentistry"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.12540000677108765,"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/T10522","display_name":"Medical Imaging Techniques and Applications","score":0.10490000247955322,"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/feature","display_name":"Feature (linguistics)","score":0.30000001192092896},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.2985999882221222},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.2476000040769577},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.24369999766349792},{"id":"https://openalex.org/keywords/quality","display_name":"Quality (philosophy)","score":0.22310000658035278}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3138999938964844},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3050000071525574},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2476000040769577},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.24369999766349792},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.22310000658035278},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.21930000185966492},{"id":"https://openalex.org/C180938184","wikidata":"https://www.wikidata.org/wiki/Q2142270","display_name":"Liquation","level":3,"score":0.20980000495910645},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2069000005722046}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:tubiblio.ulb.tu-darmstadt.de:131796","is_oa":false,"landing_page_url":"http://tubiblio.ulb.tu-darmstadt.de/131796/","pdf_url":null,"source":{"id":"https://openalex.org/S4377196390","display_name":"TUbilio (Technical University of Darmstadt)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31512782","host_organization_name":"Technische Universit\u00e4t Darmstadt","host_organization_lineage":["https://openalex.org/I31512782"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Dissertation"},{"id":"pmh:oai:tuprints.ulb.tu-darmstadt.de:20727","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401590","display_name":"Technischen Universit\u00e4t Darmstadt","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31512782","host_organization_name":"Technische Universit\u00e4t Darmstadt","host_organization_lineage":["https://openalex.org/I31512782"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},{"id":"doi:10.26083/tuprints-00020727","is_oa":true,"landing_page_url":"https://doi.org/10.26083/tuprints-00020727","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Dissertation"}],"best_oa_location":{"id":"pmh:oai:tuprints.ulb.tu-darmstadt.de:20727","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306401590","display_name":"Technischen Universit\u00e4t Darmstadt","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31512782","host_organization_name":"Technische Universit\u00e4t Darmstadt","host_organization_lineage":["https://openalex.org/I31512782"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nd","license_id":"https://openalex.org/licenses/cc-by-nd","version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/doctoralThesis"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Oral":[0],"health":[1],"issues":[2],"like":[3,118],"tooth":[4],"decay":[5],"affect":[6],"billions":[7],"of":[8,19,26,62,85,91,109,123,166,179,194,209,232,265,276,286,297,326,348,363,379,405,416,433,445,461],"people":[9],"worldwide.":[10],"The":[11,59,307,366,400,429,450],"early":[12],"detection":[13,459],"as":[14,16,39],"well":[15],"the":[17,30,56,83,105,113,129,132,163,180,195,200,206,230,241,252,266,272,284,295,314,319,327,336,346,351,360,370,377,384,465],"treatment":[18],"those":[20,394],"problems":[21],"is":[22,65,104,216,219,268,335],"an":[23,153,440],"important":[24],"part":[25],"today's":[27],"healthcare.":[28],"Since":[29],"teeth":[31,111,134,147,167,181,197,253,298,311,316,328,340,349,401,408,435],"cannot":[32],"be":[33,90],"assessed":[34],"by":[35,247,270,329,382],"visual":[36],"inspection":[37],"only":[38,135,358],"they":[40],"are":[41,52],"partly":[42],"embedded":[43],"in":[44,112,171,222,245,258,409,453,464],"soft":[45],"tissue":[46],"and":[47,68,72,107,121,128,149,169,175,192,211,243,255,304,350,372,389,392,403,420],"bones,":[48],"medical":[49,95],"imaging":[50],"techniques":[51],"employed":[53],"to":[54,70,94,100,137,144,151,226,239,288,290,321,344,354,375],"provide":[55,101],"required":[57],"information.":[58],"manual":[60],"analysis":[61,231],"such":[63,102],"images":[64,455],"time":[66],"consuming":[67],"prone":[69],"inter-":[71],"intra-observer":[73],"variability.":[74],"For":[75],"this":[76,157,159],"reason,":[77],"computer":[78],"aided":[79],"systems":[80,103],"which":[81,186,218],"automate":[82],"extraction":[84,296],"clinically":[86,466],"relevant":[87,467],"information":[88],"can":[89],"great":[92],"benefit":[93],"professionals.":[96],"A":[97],"mandatory":[98],"step":[99],"segmentation":[106,168,191,210,242,274,371,402],"numbering":[108,170,193,244,373,404],"individual":[110,154,407],"digital":[114],"images.":[115,235,292],"However,":[116],"challenges":[117],"image":[119],"quality":[120],"characteristics":[122],"dental":[124,233],"radiographs,":[125],"patient-specific":[126],"variations,":[127],"fact":[130],"that":[131,342],"32":[133],"belong":[136],"4":[138],"different":[139,426],"types":[140],"makes":[141],"it":[142],"hard":[143],"accurately":[145],"detect":[146],"boundaries":[148],"difficult":[150],"label":[152],"tooth.":[155],"In":[156],"context,":[158],"thesis":[160,237],"focuses":[161],"on":[162,188,359,424],"research":[164],"questions":[165],"panoramic":[172,302,410],"X-ray":[173],"images,":[174],"image-based":[176,309,339,430],"3-D":[177,310,431],"reconstruction":[178,312,341,357,432],"from":[182,300,318,436],"five":[183,361,437],"colored":[184,305],"photographs":[185,320,362,438],"relies":[187],"accurate":[189],"object-level":[190],"2-D":[196,246],"outlines":[198],"(in":[199],"photographs).":[201],"As":[202],"both":[203,301],"topics":[204],"share":[205],"common":[207],"aspect":[208],"numbering,":[212],"a":[213,223,259,323,331,356,457],"general":[214],"concept":[215,374],"presented":[217],"also":[220],"applied":[221],"third":[224],"application":[225],"localize":[227],"landmarks":[228,381,388],"for":[229],"cephalometric":[234,454],"This":[236,293],"proposes":[238],"solve":[240],"encoding":[248],"prior":[249],"knowledge":[250],"about":[251],"shapes":[254],"spatial":[256,385],"relations":[257],"coupled":[260],"shape":[261],"model.":[262],"Initial":[263],"placement":[264],"model":[267,289,325],"performed":[269],"exploiting":[271,383],"semantic":[273],"performance":[275],"neural":[277],"networks":[278],"while":[279],"dynamic":[280],"adaptation":[281],"strategies":[282],"increase":[283],"robustness":[285],"fitting":[287],"unseen":[291],"enables":[294],"contours":[299,317],"radiographs":[303,411],"photographs.":[306],"proposed":[308],"utilizes":[313,369],"numbered":[315],"deform":[322],"mean":[324],"minimizing":[330],"silhouette-based":[332],"loss.":[333],"It":[334],"first":[337,352],"fully-automatic":[338],"aims":[343],"reconstruct":[345],"majority":[347],"approach":[353],"perform":[355],"orthodontic":[364],"records.":[365],"landmark":[367,451],"localization":[368,452],"predict":[376],"location":[378],"19":[380],"relation":[386],"between":[387],"other":[390],"structures":[391],"refines":[393],"predictions":[395],"using":[396],"landmark-specific":[397],"Hough":[398],"Forests.":[399],"28":[406],"achieves":[412,439],"average":[413,441],"F1":[414],"scores":[415],"0.823":[417],"\u00b1":[418,422,447],"0.189":[419],"0.833":[421],"0.108":[423],"two":[425],"test":[427],"sets.":[428],"24":[434],"symmetric":[442],"surface":[443],"distance":[444],"0.807":[446],"0.379":[448],"mm.":[449],"reaches":[456],"success":[458],"rate":[460],"76.04":[462],"%":[463],"2.0":[468],"mm":[469],"accuracy":[470],"range.":[471]},"counts_by_year":[],"updated_date":"2026-08-08T01:25:22.217667","created_date":"2025-10-10T00:00:00"}