{"id":"W2016452392","doi":"10.1016/j.compmedimag.2005.10.007","title":"An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments","year":2006,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Pattern recognition (psychology); Scale-space segmentation; Support vector machine; Segmentation-based object categorization; Image segmentation; Classifier (UML); Computer-aided; Principal component analysis; Level set method; Computer vision; Feature extraction","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":[0.001861704,0.0002398296,0.0004627554,0.000500779,0.0001515374,0.0002644185,0.0006758496,0.0001868746,0.00002502679],"category_scores_gemma":[0.0001413354,0.0002353487,0.0001790004,0.0007606337,0.0002942886,0.0004616586,0.0002234246,0.0003588971,0.000002509948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004504259,"about_ca_system_score_gemma":0.00008356894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005311357,"about_ca_topic_score_gemma":0.00001392176,"domain_scores_codex":[0.9965875,0.0004181265,0.001018876,0.0007427234,0.0008711112,0.0003616239],"domain_scores_gemma":[0.9980427,0.0008735208,0.0002615048,0.0004287399,0.00004676405,0.0003467889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005199583,0.002063285,0.2250745,0.0001404483,0.0005007643,0.0001466582,0.0008220185,0.0004312313,0.0007705159,0.05009716,0.002549555,0.7173519],"study_design_scores_gemma":[0.001402526,0.00007538271,0.1896973,0.00005578056,0.00006115796,0.00001295419,0.00001327727,0.7947646,0.00008978182,0.01356744,0.0000479975,0.0002118326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03254452,0.00005046156,0.9656052,0.0007998439,0.0004051299,0.0003803466,0.00001892785,0.0001924973,0.000003072497],"genre_scores_gemma":[0.1281254,0.0000463237,0.8680363,0.003120451,0.000278938,0.00005993234,0.0003139081,0.00001460199,0.000004180727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7943334,"threshold_uncertainty_score":0.9597242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02886535993748987,"score_gpt":0.3516194388672539,"score_spread":0.322754078929764,"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."}}