{"id":"W2063351862","doi":"10.1115/sbc2013-14704","title":"Direct Structured Finite Element Mesh Generation From Three-Dimensional Medical Images","year":2013,"lang":"en","type":"article","venue":"Volume 1A: Abdominal Aortic Aneurysms; Active and Reactive Soft Matter; Atherosclerosis; BioFluid Mechanics; Education; Biotransport Phenomena; Bone, Joint and Spine Mechanics; Brain Injury; Cardiac Mechanics; Cardiovascular Devices, Fluids and Imaging; Cartilage and Disc Mechanics; Cell and Tissue Engineering; Cerebral Aneurysms; Computational Biofluid Dynamics; Device Design, Human Dynamics, and Rehabilitation; Drug Delivery and Disease Treatment; Engineered Cellular Environments","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Finite element method; Mesh generation; Construct (python library); Computer science; Medical imaging; Computer vision; Algorithm; Artificial intelligence; Structural engineering; Engineering; Programming language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005511453,0.0005690698,0.0005047445,0.0009486778,0.0002291055,0.0006895087,0.0008545496,0.0009745038,0.00235061],"category_scores_gemma":[0.001891415,0.0005366085,0.0007901437,0.0004015545,0.0005631464,0.0005665227,0.0008374234,0.0007185487,0.001161424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002635731,"about_ca_system_score_gemma":0.0006098737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000571155,"about_ca_topic_score_gemma":0.001141219,"domain_scores_codex":[0.9996995,0.0000555425,0.00002390832,0.00004363753,0.0001621175,0.00001536247],"domain_scores_gemma":[0.9993338,0.000362895,0.00005437263,0.0001082114,0.0001214713,0.00001928787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001351359,0.00009722439,0.001206889,0.0004578887,0.00007387256,0.0003429405,0.000436758,0.4631442,0.1321229,0.02170686,0.003988746,0.3762867],"study_design_scores_gemma":[0.00001329788,0.00004800937,0.0002210797,0.00002658806,0.000009847009,0.0002187334,0.00003758697,0.9677623,0.01911781,0.007215471,0.005313188,0.000016135],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001828103,0.00001884821,0.9974775,0.0000265958,0.000009312733,0.00002799638,0.00002323693,0.0002668708,0.000321636],"genre_scores_gemma":[0.05839148,0.0001044239,0.9397095,0.00007012036,0.00001234297,0.000150051,0.0002527468,0.0002104352,0.00109898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00235061,"threshold_uncertainty_score":0.007863522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004649094970792246,"score_gpt":0.1828852157015715,"score_spread":0.1782361207307793,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}