{"id":"https://openalex.org/W4416500704","doi":"https://doi.org/10.1007/s00138-025-01717-5","title":"Deconstruct to reconstruct: an automated pipeline for parsing complex CT assemblies","display_name":"Deconstruct to reconstruct: an automated pipeline for parsing complex CT assemblies","publication_year":2025,"publication_date":"2025-11-21","ids":{"openalex":"https://openalex.org/W4416500704","doi":"https://doi.org/10.1007/s00138-025-01717-5"},"language":"en","primary_location":{"id":"doi:10.1007/s00138-025-01717-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00138-025-01717-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01717-5.pdf","source":{"id":"https://openalex.org/S27728525","display_name":"Machine Vision and Applications","issn_l":"0932-8092","issn":["0932-8092","1432-1769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Vision and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01717-5.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5005369273","display_name":"Peter Lippmann","orcid":null},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]},{"id":"https://openalex.org/I85384741","display_name":"Heidelberg University","ror":"https://ror.org/04rcqnp59","country_code":"US","type":"education","lineage":["https://openalex.org/I85384741"]}],"countries":["DE","US"],"is_corresponding":true,"raw_author_name":"Peter Lippmann","raw_affiliation_strings":["IWR, Heidelberg University, Heidelberg, 69120, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IWR, Heidelberg University, Heidelberg, 69120, Germany","institution_ids":["https://openalex.org/I223822909","https://openalex.org/I85384741"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014813160","display_name":"Roman Remme","orcid":"https://orcid.org/0009-0004-7433-7041"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]},{"id":"https://openalex.org/I85384741","display_name":"Heidelberg University","ror":"https://ror.org/04rcqnp59","country_code":"US","type":"education","lineage":["https://openalex.org/I85384741"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Roman Remme","raw_affiliation_strings":["IWR, Heidelberg University, Heidelberg, 69120, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IWR, Heidelberg University, Heidelberg, 69120, Germany","institution_ids":["https://openalex.org/I223822909","https://openalex.org/I85384741"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020048224","display_name":"Fred A. Hamprecht","orcid":"https://orcid.org/0000-0003-4148-5043"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]},{"id":"https://openalex.org/I85384741","display_name":"Heidelberg University","ror":"https://ror.org/04rcqnp59","country_code":"US","type":"education","lineage":["https://openalex.org/I85384741"]}],"countries":["DE","US"],"is_corresponding":false,"raw_author_name":"Fred A. Hamprecht","raw_affiliation_strings":["IWR, Heidelberg University, Heidelberg, 69120, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"IWR, Heidelberg University, Heidelberg, 69120, Germany","institution_ids":["https://openalex.org/I223822909","https://openalex.org/I85384741"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5005369273"],"corresponding_institution_ids":["https://openalex.org/I223822909","https://openalex.org/I85384741"],"apc_list":{"value":2490,"currency":"EUR","value_usd":3090},"apc_paid":{"value":2490,"currency":"EUR","value_usd":3090},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.33093821,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"37","issue":"1","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":0.3758000135421753,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10719","display_name":"3D Shape Modeling and Analysis","score":0.3758000135421753,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.15399999916553497,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.07880000025033951,"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/pipeline","display_name":"Pipeline (software)","score":0.8740000128746033},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.6834999918937683},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6165000200271606},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5759999752044678},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.47679999470710754},{"id":"https://openalex.org/keywords/computed-tomography","display_name":"Computed tomography","score":0.43630000948905945},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3781999945640564}],"concepts":[{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.8740000128746033},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354000210762024},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.6834999918937683},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6165000200271606},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6050999760627747},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5759999752044678},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C544519230","wikidata":"https://www.wikidata.org/wiki/Q32566","display_name":"Computed tomography","level":2,"score":0.43630000948905945},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4072999954223633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.364300012588501},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2897000014781952},{"id":"https://openalex.org/C5134670","wikidata":"https://www.wikidata.org/wiki/Q1626444","display_name":"Cut","level":4,"score":0.28850001096725464},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C3019831412","wikidata":"https://www.wikidata.org/wiki/Q5778278","display_name":"Fully automatic","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C3019060180","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automated method","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s00138-025-01717-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00138-025-01717-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01717-5.pdf","source":{"id":"https://openalex.org/S27728525","display_name":"Machine Vision and Applications","issn_l":"0932-8092","issn":["0932-8092","1432-1769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Vision and Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s00138-025-01717-5","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s00138-025-01717-5","pdf_url":"https://link.springer.com/content/pdf/10.1007/s00138-025-01717-5.pdf","source":{"id":"https://openalex.org/S27728525","display_name":"Machine Vision and Applications","issn_l":"0932-8092","issn":["0932-8092","1432-1769"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Machine Vision and Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2575772051","display_name":null,"funder_award_id":"240245660","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G4202865901","display_name":null,"funder_award_id":"EXC 2181/1 -390900948","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6509180224","display_name":null,"funder_award_id":"SFB 1129","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G6526081583","display_name":null,"funder_award_id":"EXC 2181","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G680210925","display_name":null,"funder_award_id":"Projektnummer 240245660","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"},{"id":"https://openalex.org/G7105266721","display_name":null,"funder_award_id":"390900948","funder_id":"https://openalex.org/F4320320879","funder_display_name":"Deutsche Forschungsgemeinschaft"}],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"},{"id":"https://openalex.org/F4320320881","display_name":"Universit\u00e4t Heidelberg","ror":"https://ror.org/038t36y30"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4416500704.pdf","grobid_xml":"https://content.openalex.org/works/W4416500704.grobid-xml"},"referenced_works_count":62,"referenced_works":["https://openalex.org/W138878435","https://openalex.org/W151895561","https://openalex.org/W1606858007","https://openalex.org/W1901041506","https://openalex.org/W1901129140","https://openalex.org/W1981264533","https://openalex.org/W1985241352","https://openalex.org/W1993052292","https://openalex.org/W2001353114","https://openalex.org/W2010209818","https://openalex.org/W2025055828","https://openalex.org/W2028248716","https://openalex.org/W2045642761","https://openalex.org/W2046112356","https://openalex.org/W2057253725","https://openalex.org/W2082390334","https://openalex.org/W2083624955","https://openalex.org/W2106629076","https://openalex.org/W2124561101","https://openalex.org/W2138147909","https://openalex.org/W2146807782","https://openalex.org/W2149961050","https://openalex.org/W2150066425","https://openalex.org/W2150593711","https://openalex.org/W2151103935","https://openalex.org/W2158964841","https://openalex.org/W2160821342","https://openalex.org/W2161168419","https://openalex.org/W2295332248","https://openalex.org/W2473011025","https://openalex.org/W2519911873","https://openalex.org/W2553307952","https://openalex.org/W2739457533","https://openalex.org/W2896289918","https://openalex.org/W2898323380","https://openalex.org/W2910628332","https://openalex.org/W2942082708","https://openalex.org/W2962914239","https://openalex.org/W2963666542","https://openalex.org/W2963727135","https://openalex.org/W2963744743","https://openalex.org/W2981995220","https://openalex.org/W2984567007","https://openalex.org/W2999219213","https://openalex.org/W3034681945","https://openalex.org/W3046117255","https://openalex.org/W3096609285","https://openalex.org/W3103544346","https://openalex.org/W3111598592","https://openalex.org/W3120768930","https://openalex.org/W3166470370","https://openalex.org/W3179877418","https://openalex.org/W3211025827","https://openalex.org/W3217122878","https://openalex.org/W4249866455","https://openalex.org/W4281723238","https://openalex.org/W4286210182","https://openalex.org/W4312546175","https://openalex.org/W4312727231","https://openalex.org/W4313120576","https://openalex.org/W4313458947","https://openalex.org/W4386066002"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"Many":[1],"technical":[2,88],"products":[3],"are":[4,160],"assemblies":[5,14,45,89],"formed":[6],"from":[7,46],"smaller,":[8],"versatile":[9],"building":[10],"blocks.":[11],"Deconstructing":[12],"such":[13],"is":[15,188,223],"an":[16,21,34,192],"industrially":[17],"important":[18],"problem":[19],"and":[20,36,58,99,108,125,162,212],"inspiring":[22],"challenge":[23],"for":[24,40],"machine":[25],"learning":[26],"approaches.":[27],"For":[28],"the":[29,119,153,186,195],"first":[30],"time,":[31],"we":[32],"present":[33],"effective":[35],"fully":[37],"automated":[38],"pipeline":[39,116,133,222],"parsing":[41],"large-scale,":[42],"complex":[43,130],"3D":[44,136,154],"computed":[47],"tomography":[48],"(CT)":[49],"scans":[50,69,80,146],"into":[51],"their":[52],"individual":[53],"parts.":[54],"We":[55,201],"have":[56],"generated":[57],"make":[59],"available":[60],"a":[61,135,165,171],"high-quality":[62],"dataset":[63],"of":[64,76,86,129,173,179,194],"simulated,":[65],"physically":[66],"accurate":[67],"CT":[68,79,145,209,219],"with":[70,90,97,147,164],"ground":[71],"truth":[72],"annotations.":[73],"It":[74],"consists":[75],"seven":[77],"high-resolution":[78,120],"(":[81],"$$\\sim":[82],"\\!":[83],"2000^3$$":[84],"voxels)":[85],"different":[87,208],"up":[91],"to":[92,151,169,207,225,238],"3600":[93],"parts,":[94],"each":[95],"annotated":[96],"instance":[98,158,193],"semantic":[100],"labels.":[101],"The":[102,132,156,177],"parts":[103,229],"strongly":[104],"vary":[105],"in":[106,111],"size":[107],"sometimes":[109],"differ":[110],"fine":[112],"details":[113],"only.":[114],"Our":[115,221],"successfully":[117],"handles":[118],"volumetric":[121],"inputs":[122],"(3\u201330":[123],"GB)":[124],"produces":[126],"detailed":[127],"reconstructions":[128],"assemblies.":[131],"combines":[134],"deep":[137],"boundary":[138],"detection":[139],"network":[140],"trained":[141],"only":[142],"on":[143,217],"simulated":[144],"efficient":[148],"graph":[149],"partitioning":[150],"segment":[152],"scans.":[155,220],"predicted":[157],"segments":[159],"matched":[161],"aligned":[163],"known":[166],"part":[167,175],"catalog":[168],"form":[170],"set":[172,199],"candidate":[174],"poses.":[176],"subset":[178],"these":[180],"proposals":[181],"that":[182,203,227],"jointly":[183],"best":[184],"reconstructs":[185],"assembly":[187],"found":[189],"by":[190],"solving":[191],"maximum":[196],"weighted":[197],"independent":[198],"problem.":[200],"demonstrate":[202],"our":[204,235],"approach":[205,236],"generalizes":[206],"scan":[210],"setups":[211],"yields":[213],"promising":[214],"results":[215],"even":[216],"real":[218],"applicable":[224],"models":[226],"include":[228],"not":[230],"seen":[231],"during":[232],"training,":[233],"making":[234],"adaptable":[237],"real-world":[239],"scenarios.":[240]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-11-23T00:00:00"}