{"id":"W2127912520","doi":"10.1109/iembs.2007.4352410","title":"Airway Segmentation and Measurement in CT Images","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Vector flow; Segmentation; Artificial intelligence; Computer vision; Computer science; Image segmentation; Edge detection; Computed tomography; Airway; Cone beam ct; Volume (thermodynamics); 3d model; Pattern recognition (psychology); Heuristic; Image (mathematics); Image processing; Radiology; Medicine; Physics","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":[0.001015013,0.0005713609,0.0006340164,0.001944495,0.0004343747,0.001510206,0.000839288,0.001818693,0.001650572],"category_scores_gemma":[0.004561913,0.0007969642,0.0004630483,0.001313576,0.0007431375,0.001592179,0.0008708228,0.0007704158,0.000946758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004289042,"about_ca_system_score_gemma":0.0006793257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001799901,"about_ca_topic_score_gemma":0.001641044,"domain_scores_codex":[0.99892,0.0002460251,0.00007093947,0.0001994498,0.0005026351,0.00006099755],"domain_scores_gemma":[0.9990301,0.0004651236,0.0001502661,0.0001153299,0.0001904442,0.00004868715],"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":[0.0003419147,0.00007490675,0.006840059,0.0004983591,0.000041083,0.0005780262,0.0004550345,0.03218061,0.4718747,0.007410344,0.001879188,0.4778259],"study_design_scores_gemma":[0.00004282552,0.0005736793,0.03470692,0.0003097983,0.00008291317,0.004283918,0.0003288239,0.5205466,0.3999693,0.01497555,0.02398776,0.0001918642],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02747365,0.0009740736,0.9685802,0.0001421715,0.00004670954,0.0001228978,0.0001443483,0.001336732,0.001179183],"genre_scores_gemma":[0.2098197,0.001376513,0.7867654,0.0001242289,0.00007408703,0.0002141854,0.0002778615,0.0003287151,0.00101929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001944495,"threshold_uncertainty_score":0.005521715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03425797640120814,"score_gpt":0.2868228533409519,"score_spread":0.2525648769397437,"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."}}