{"id":"https://openalex.org/W7225825888","doi":"https://doi.org/10.21203/rs.3.rs-11685202/v1","title":"Learned Bias Correction for Surface-Area Estimation from CT-like Voxel Data","display_name":"Learned Bias Correction for Surface-Area Estimation from CT-like Voxel Data","publication_year":2026,"publication_date":"2026-11-11","ids":{"openalex":"https://openalex.org/W7225825888","doi":"https://doi.org/10.21203/rs.3.rs-11685202/v1"},"language":"en","primary_location":{"id":"doi:10.21203/rs.3.rs-11685202/v1","doi":"https://doi.org/10.21203/rs.3.rs-11685202/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11685202/v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.21203/rs.3.rs-11685202/v1","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5002408538","display_name":"Manas Jain","orcid":"https://orcid.org/0009-0004-1739-324X","observed_orcids":["https://orcid.org/0009-0004-1739-324X"]},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Manas Jain","raw_affiliation_strings":["Indian Institute of Technology Roorkee"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee","institution_ids":["https://openalex.org/I154851008"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080620426","display_name":"Mayank Goswami","orcid":"https://orcid.org/0000-0001-8829-9980","observed_orcids":["https://orcid.org/0000-0001-8829-9980"]},"institutions":[{"id":"https://openalex.org/I154851008","display_name":"Indian Institute of Technology Roorkee","ror":"https://ror.org/00582g326","country_code":"IN","type":"education","lineage":["https://openalex.org/I154851008"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Mayank Goswami","raw_affiliation_strings":["Indian Institute of Technology Roorkee"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Indian Institute of Technology Roorkee","institution_ids":["https://openalex.org/I154851008"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5080620426"],"corresponding_institution_ids":["https://openalex.org/I154851008"],"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":false,"primary_topic":{"id":"https://openalex.org/T10719","display_name":"3D Shape Modeling and Analysis","score":0.8583999872207642,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.8583999872207642,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"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/T12923","display_name":"Digital Image Processing Techniques","score":0.05209999904036522,"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/T10481","display_name":"Computer Graphics and Visualization Techniques","score":0.022700000554323196,"subfield":{"id":"https://openalex.org/subfields/1704","display_name":"Computer Graphics and Computer-Aided Design"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C42087268","wikidata":"https://www.wikidata.org/wiki/Q1886184","display_name":"Marching cubes","level":3,"score":0.805400013923645},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.6491000056266785},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6484000086784363},{"id":"https://openalex.org/C17866373","wikidata":"https://www.wikidata.org/wiki/Q5437042","display_name":"Fast marching method","level":2,"score":0.59579998254776},{"id":"https://openalex.org/C2776799497","wikidata":"https://www.wikidata.org/wiki/Q484298","display_name":"Surface (topology)","level":2,"score":0.5770000219345093},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.5593000054359436},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5519999861717224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5378999710083008},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5048999786376953},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.501800000667572},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4900999963283539},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.45559999346733093},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4377000033855438},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41339999437332153},{"id":"https://openalex.org/C43173174","wikidata":"https://www.wikidata.org/wiki/Q2646942","display_name":"Regular grid","level":3,"score":0.383899986743927},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.37700000405311584},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C20885615","wikidata":"https://www.wikidata.org/wiki/Q825595","display_name":"Surface reconstruction","level":3,"score":0.35569998621940613},{"id":"https://openalex.org/C138268822","wikidata":"https://www.wikidata.org/wiki/Q1051925","display_name":"Resolution (logic)","level":2,"score":0.30880001187324524},{"id":"https://openalex.org/C73621898","wikidata":"https://www.wikidata.org/wiki/Q2940504","display_name":"Distance transform","level":3,"score":0.3041999936103821},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.2838999927043915},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C45107383","wikidata":"https://www.wikidata.org/wiki/Q1311829","display_name":"Isosurface","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.26759999990463257},{"id":"https://openalex.org/C100675267","wikidata":"https://www.wikidata.org/wiki/Q1371624","display_name":"Background noise","level":2,"score":0.26660001277923584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.21203/rs.3.rs-11685202/v1","doi":"https://doi.org/10.21203/rs.3.rs-11685202/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11685202/v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.21203/rs.3.rs-11685202/v1","doi":"https://doi.org/10.21203/rs.3.rs-11685202/v1","is_oa":true,"landing_page_url":"https://doi.org/10.21203/rs.3.rs-11685202/v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306525896","display_name":"Research Square","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Research Square","raw_type":"posted-content"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Measuring":[1],"the":[2,18,29,36,40,43,60,71,75,78,129,191,200,224,231,248],"surface":[3,31,44,95,130],"area":[4,45,96,131],"of":[5,21,24,39,111,171,247],"an":[6],"object":[7],"from":[8,87,151,157,221],"a":[9,22,56,63,92],"CT":[10],"scan":[11,19],"is":[12],"harder":[13],"than":[14,99,242,255],"it":[15],"looks":[16],"because":[17],"consists":[20],"grid":[23],"small":[25],"cubes":[26],"(voxels),":[27],"while":[28],"true":[30,94],"passes":[32],"between":[33],"them.":[34],"Counting":[35],"exposed":[37],"faces":[38],"voxels":[41],"overestimates":[42],"by":[46,133],"approximately":[47,256],"50%":[48],"at":[49,109,173,178,194,203],"every":[50],"resolution.":[51,180],"Marching":[52,134,166],"Cubes,":[53],"which":[54],"fits":[55],"triangular":[57],"mesh":[58],"to":[59,97,125,128,188,230],"data,":[61],"provides":[62],"closer":[64],"estimate,":[65],"but":[66],"its":[67],"error":[68],"depends":[69],"on":[70,160,213],"object's":[72],"curvature":[73],"and":[74,113,176,197],"noise":[76,119],"in":[77],"scan.In":[79],"this":[80],"study,":[81],"we":[82],"generated":[83],"2,400":[84],"synthetic":[85],"shapes":[86,102,163],"seven":[88],"families,":[89],"each":[90],"with":[91,116],"known":[93],"better":[98],"0.01%.":[100],"These":[101],"were":[103,218],"converted":[104],"into":[105],"14,400":[106],"CT-like":[107],"volumes":[108],"resolutions":[110],"32\u00b3":[112,174,195],"64\u00b3":[114,179,204],"voxels,":[115],"three":[117,123],"different":[118],"levels.We":[120],"then":[121],"trained":[122],"models":[124,184],"predict":[126],"corrections":[127],"estimated":[132],"Cubes:":[135],"Random":[136,201,226],"Forest:":[137],"Uses":[138],"ten":[139],"simple":[140,225],"shape":[141,215],"measurements.":[142],"3D":[143,192],"Convolutional":[144],"Neural":[145],"Network":[146],"(CNN):":[147],"Learns":[148,155],"features":[149,156],"directly":[150],"volumetric":[152],"data.":[153],"PointNet:":[154],"point-cloud":[158],"representations.Testing":[159],"previously":[161],"unseen":[162],"showed":[164],"that":[165,217],"Cubes":[167],"produced":[168],"average":[169],"errors":[170,187,210],"4.78%":[172],"resolution":[175,196],"3.52%":[177],"The":[181,233],"best":[182],"correction":[183],"reduced":[185],"these":[186],"0.68%":[189],"using":[190,199],"CNN":[193,234],"0.18%":[198],"Forest":[202,227],"resolution.The":[205],"corrected":[206],"methods":[207,249],"also":[208],"maintained":[209],"below":[211],"2%":[212],"two":[214,257],"families":[216],"completely":[219],"excluded":[220],"training.":[222],"Overall,":[223],"performed":[228],"comparably":[229],"CNN.":[232],"was":[235],"more":[236],"effective":[237],"only":[238],"for":[239],"objects":[240],"smaller":[241],"16":[243],"voxels.":[244,258],"However,":[245],"none":[246],"could":[250],"reliably":[251],"recover":[252],"structures":[253],"thinner":[254]},"counts_by_year":[],"updated_date":"2026-11-12T12:28:57.059296","created_date":"2026-11-12T00:00:00"}