{"id":"W2144887288","doi":"10.1016/j.cmpb.2006.04.009","title":"Anatomical structure modeling from medical images","year":2006,"lang":"en","type":"article","venue":"Computer Methods and Programs in Biomedicine","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"U.S. National Library of Medicine; National Center for Research Resources; National Institute of Mental Health; Natural Sciences and Engineering Research Council of Canada; National Science Foundation; Institut national de recherche en informatique et en automatique (INRIA); Consortia for Improving Medicine with Innovation and Technology","keywords":"Delaunay triangulation; Tetrahedron; Computer science; Artificial intelligence; Computer vision; Set (abstract data type); Marching cubes; Triangulation; Medical imaging; 3D modeling; Algorithm; Mathematics; Visualization; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004712615,0.0008887143,0.0008243117,0.00150458,0.0003671344,0.001383237,0.001432253,0.001674776,0.00208617],"category_scores_gemma":[0.002027271,0.0009996912,0.001593605,0.001136036,0.0005267019,0.0008662624,0.0009182342,0.001269005,0.001865151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005158613,"about_ca_system_score_gemma":0.001145039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004353609,"about_ca_topic_score_gemma":0.004758408,"domain_scores_codex":[0.9997063,0.00004465423,0.00001576106,0.00006235036,0.0001507097,0.00002011423],"domain_scores_gemma":[0.9996761,0.0001405784,0.00003776515,0.0000642587,0.00006429892,0.00001690864],"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":[0.0001087505,0.00006887135,0.0008422135,0.0003882439,0.0001203867,0.000545054,0.0001964872,0.6707557,0.04924301,0.02207655,0.005038621,0.2506161],"study_design_scores_gemma":[0.000008114897,0.00002567488,0.0002840816,0.00003012616,0.0000339337,0.0004350996,0.00001918767,0.9721988,0.01131872,0.01004398,0.005585642,0.0000165437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003622182,0.0002907928,0.9940677,0.000133964,0.00003252607,0.00005439269,0.0001361789,0.001006434,0.000655946],"genre_scores_gemma":[0.2479029,0.003545401,0.7366875,0.0002554789,0.0001465545,0.000455509,0.001574809,0.001083915,0.008347934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004353609,"threshold_uncertainty_score":0.008656561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03159999159922582,"score_gpt":0.368739441009692,"score_spread":0.3371394494104662,"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."}}