{"id":"W2167791816","doi":"10.1016/s0895-6111(01)00016-7","title":"A flexible image segmentation prior to parametric estimation","year":2001,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Positron emission tomography; Pixel; Parametric statistics; Segmentation; Nuclear medicine; Blood flow; Artificial intelligence; Fluorodeoxyglucose; Image segmentation; Mathematics; Image (mathematics); Computer science; Pattern recognition (psychology); Physics; Medicine; Statistics; Internal medicine","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":[0.0004449124,0.0001581768,0.0002739387,0.0003425945,0.0001470861,0.00007535488,0.0001224621,0.00007400734,0.00008483445],"category_scores_gemma":[0.0004477201,0.0001363179,0.00006475643,0.000944918,0.0002026181,0.0001011899,0.0001060631,0.0002968209,0.00002341529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000267377,"about_ca_system_score_gemma":0.00008079088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003360055,"about_ca_topic_score_gemma":4.436605e-7,"domain_scores_codex":[0.9984639,0.00003657482,0.0003334058,0.0003539788,0.0005341316,0.0002780572],"domain_scores_gemma":[0.9986789,0.0001393084,0.00006122926,0.000275025,0.0001138351,0.0007317344],"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":[0.0001088079,0.0004028711,0.00546629,0.0001757659,0.00004042405,0.0001951709,0.0002030823,0.000002372167,0.008086891,0.002805039,0.03211997,0.9503933],"study_design_scores_gemma":[0.004728823,0.0002715537,0.02168781,0.0009704024,0.0002090827,0.001918162,0.00008261425,0.87393,0.0028509,0.005513615,0.08726233,0.0005746824],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1308951,0.0001391046,0.8235111,0.04414446,0.0001005553,0.0004859161,0.000002206212,0.000446709,0.0002748453],"genre_scores_gemma":[0.445004,0.000874563,0.5371642,0.01629943,0.0002358372,0.0001348114,0.00009261643,0.00003453103,0.000160023],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9498186,"threshold_uncertainty_score":0.5558884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02055622666683135,"score_gpt":0.3451419694624448,"score_spread":0.3245857427956135,"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."}}