{"id":"https://openalex.org/W4403787538","doi":"https://doi.org/10.48550/arxiv.2409.16058","title":"Generative 3D Cardiac Shape Modelling for In-Silico Trials","display_name":"Generative 3D Cardiac Shape Modelling for In-Silico Trials","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4403787538","doi":"https://doi.org/10.48550/arxiv.2409.16058"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2409.16058","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.16058","pdf_url":"https://arxiv.org/pdf/2409.16058","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2409.16058","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114415110","display_name":"Andrei Gasparovici","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gasparovici, Andrei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027550247","display_name":"Alex Serban","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Serban, Alex","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T14510","display_name":"Medical Imaging and Analysis","score":0.9625999927520752,"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":0.9625999927520752,"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/T11159","display_name":"Manufacturing Process and Optimization","score":0.9624000191688538,"subfield":{"id":"https://openalex.org/subfields/2209","display_name":"Industrial and Manufacturing 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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.9611999988555908,"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/in-silico","display_name":"In silico","score":0.8236752152442932},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7085490226745605},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5035516619682312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4981412887573242},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4168170392513275},{"id":"https://openalex.org/keywords/computational-biology","display_name":"Computational biology","score":0.3950710594654083},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.35990816354751587},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3398517966270447},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.24887311458587646},{"id":"https://openalex.org/keywords/genetics","display_name":"Genetics","score":0.08637386560440063}],"concepts":[{"id":"https://openalex.org/C2775905019","wikidata":"https://www.wikidata.org/wiki/Q192572","display_name":"In silico","level":3,"score":0.8236752152442932},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7085490226745605},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5035516619682312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4981412887573242},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4168170392513275},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.3950710594654083},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.35990816354751587},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3398517966270447},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.24887311458587646},{"id":"https://openalex.org/C54355233","wikidata":"https://www.wikidata.org/wiki/Q7162","display_name":"Genetics","level":1,"score":0.08637386560440063},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2409.16058","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.16058","pdf_url":"https://arxiv.org/pdf/2409.16058","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2409.16058","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2409.16058","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2409.16058","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2409.16058","pdf_url":"https://arxiv.org/pdf/2409.16058","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G870349561","display_name":null,"funder_award_id":"101017578","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4403787538.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4287117424","https://openalex.org/W4387506531","https://openalex.org/W2087346071","https://openalex.org/W2967848559","https://openalex.org/W4299831724","https://openalex.org/W4283803360"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,22,29,48],"deep":[3],"learning":[4],"method":[5],"to":[6,73],"model":[7,82],"and":[8,68],"generate":[9,100],"synthetic":[10],"aortic":[11,51,85],"shapes":[12,16,86,102],"based":[13],"on":[14,47,64],"representing":[15],"as":[17],"the":[18,37,60,94],"zero-level":[19],"set":[20],"of":[21,31,40,50],"neural":[23,61],"signed":[24],"distance":[25],"field,":[26],"conditioned":[27],"by":[28,58,91],"family":[30],"trainable":[32],"embedding":[33,96],"vectors":[34],"with":[35,87],"encode":[36],"geometric":[38],"features":[39],"each":[41],"shape.":[42],"The":[43],"network":[44],"is":[45],"trained":[46],"dataset":[49],"root":[52],"meshes":[53],"reconstructed":[54],"from":[55,93],"CT":[56],"images":[57],"making":[59],"field":[62],"vanish":[63],"sampled":[65],"surface":[66],"points":[67],"enforcing":[69],"its":[70],"spatial":[71],"gradient":[72],"have":[74],"unit":[75],"norm.":[76],"Empirical":[77],"results":[78],"show":[79],"that":[80,103],"our":[81],"can":[83,99,109],"represent":[84],"high":[88],"fidelity.":[89],"Moreover,":[90],"sampling":[92],"learned":[95],"vectors,":[97],"we":[98],"novel":[101],"resemble":[104],"real":[105],"patient":[106],"anatomies,":[107],"which":[108],"be":[110],"used":[111],"for":[112],"in-silico":[113],"trials.":[114]},"counts_by_year":[],"updated_date":"2026-06-14T07:44:22.658603","created_date":"2025-10-10T00:00:00"}
