{"id":"W1596933240","doi":"10.1109/iembs.2003.1279808","title":"Fast reconstruction of volumetric models of anatomical structures","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Delaunay triangulation; Computer vision; Artificial intelligence; Computer science; Triangulation; 3D reconstruction; Segmentation; Surface reconstruction; Smoothing; Process (computing); Constrained Delaunay triangulation; Visual hull; Iterative reconstruction; Tetrahedron; Image segmentation; Marching cubes; Algorithm; Mathematics; Surface (topology); Visualization; Geometry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001067492,0.00005288448,0.0001239649,0.000228367,0.00001266534,0.00001127972,0.000331848,0.00004410818,0.00005612607],"category_scores_gemma":[0.00004530731,0.00004459328,0.00003907263,0.0005218703,0.000101741,0.0004027763,0.00007282414,0.00005664144,0.000001365193],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002792539,"about_ca_system_score_gemma":0.0000561705,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001126067,"about_ca_topic_score_gemma":0.000002453645,"domain_scores_codex":[0.9992342,0.00002046037,0.0002718115,0.0001439574,0.0002482487,0.00008134838],"domain_scores_gemma":[0.9994841,0.00002487554,0.0001124227,0.0002261435,0.0001029826,0.00004949454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003930859,0.00005490112,0.0002025673,0.00002996401,0.00001439323,0.000001243234,0.0002355458,0.0007936886,0.02665942,0.1984449,0.0001809454,0.7733785],"study_design_scores_gemma":[0.0002710379,0.00006610888,0.0006195621,0.00001325864,0.000002736197,0.00001646983,0.00003523447,0.02506084,0.8125839,0.1612695,0.000001167102,0.00006014695],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04616505,0.0000220007,0.9521488,0.0000594961,0.00007697823,0.00007369445,0.000001137317,0.00008550539,0.001367325],"genre_scores_gemma":[0.5141466,0.000006766003,0.4857918,0.00003488165,0.000004639754,0.000001080389,3.725713e-7,0.000001464402,0.00001239615],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7859245,"threshold_uncertainty_score":0.1818461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01704886714608085,"score_gpt":0.2666759077269421,"score_spread":0.2496270405808613,"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."}}