{"id":"W1987982102","doi":"10.2200/s00185ed1v01y200903bme030","title":"Landmarking and Segmentation of 3D CT Images","year":2009,"lang":"en","type":"article","venue":"Synthesis lectures on biomedical engineering","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"","keywords":"Segmentation; Diaphragm (acoustics); Context (archaeology); Medicine; Vertebral column; Radiology; Computer science; Artificial intelligence; Anatomy; Biology","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":[0.0001426732,0.0001003914,0.0001956407,0.000138167,0.00002430444,0.00000784259,0.00005030512,0.00004070794,0.00005322719],"category_scores_gemma":[0.0005352424,0.00007601172,0.00003544486,0.0001375018,0.00005563035,0.00001854336,0.0000105642,0.0001396903,0.000001529902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002014005,"about_ca_system_score_gemma":0.00001382728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005746823,"about_ca_topic_score_gemma":4.838668e-8,"domain_scores_codex":[0.9992992,0.000008676358,0.0001771555,0.0001513724,0.000224594,0.0001390389],"domain_scores_gemma":[0.9994519,0.0002294656,0.00003455817,0.0001341532,0.00001550356,0.000134442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002489702,0.0001313124,0.0001329467,0.0001314076,0.00003194942,0.0000210026,0.0000362607,0.00001420852,0.7531403,0.0003740774,0.001797472,0.2441642],"study_design_scores_gemma":[0.0006437518,0.0004211026,0.01265449,0.0009839246,0.0001421089,0.0001657788,0.0000168775,0.01255493,0.9583104,0.0002367626,0.01359846,0.0002714612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8175327,0.0009515902,0.1436993,0.0336725,0.000149907,0.0009051,0.00003400179,0.000874652,0.002180317],"genre_scores_gemma":[0.9645111,0.00009943002,0.0348682,0.00039182,0.00007871913,0.0000172681,0.000007790752,0.000009774196,0.00001588861],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2438927,"threshold_uncertainty_score":0.3099668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007823495870960496,"score_gpt":0.2651116350414766,"score_spread":0.2572881391705161,"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."}}