{"id":"W1994804254","doi":"10.1016/j.neuroimage.2011.06.054","title":"Appearance-based modeling for segmentation of hippocampus and amygdala using multi-contrast MR imaging","year":2011,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Segmentation; Artificial intelligence; Sørensen–Dice coefficient; Contrast (vision); Pattern recognition (psychology); Active appearance model; Computer science; Weighting; Image segmentation; Voxel; Partial volume; Computer vision; Mathematics; Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002742126,0.0001213744,0.0001518579,0.0001225973,0.00007725663,0.00005502121,0.0002622003,0.00002598355,0.00000456823],"category_scores_gemma":[0.00007861923,0.0001245126,0.0000464031,0.000131434,0.00008317751,0.0005806186,0.00006594983,0.00007828286,8.468456e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000187568,"about_ca_system_score_gemma":0.00004184518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005316423,"about_ca_topic_score_gemma":0.000001369351,"domain_scores_codex":[0.9989206,0.00006577859,0.0002943309,0.0003378213,0.0001810511,0.0002004418],"domain_scores_gemma":[0.9993669,0.00004736127,0.0001418859,0.0002518449,0.0001144443,0.00007750146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003211272,0.0001464349,0.001977147,0.0001769781,0.000008191369,0.00001483044,0.001061883,0.0003089547,0.7947281,0.0001513237,0.00002082564,0.2013732],"study_design_scores_gemma":[0.0007417625,0.00003720368,0.0003965438,0.00004590274,0.000008944348,0.000005142276,0.00002953984,0.7800299,0.2179539,0.0006588546,7.118632e-7,0.00009159968],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1023264,0.00007879006,0.8968999,0.00004150311,0.00009872522,0.0003837543,0.000003958817,0.0001362848,0.00003068708],"genre_scores_gemma":[0.5098947,0.000004026102,0.4897732,0.0002916164,0.000009106157,0.00001478239,0.000001277463,0.00000939095,0.000001833228],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7797209,"threshold_uncertainty_score":0.5077477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07852845862408922,"score_gpt":0.3065548098609314,"score_spread":0.2280263512368422,"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."}}