{"id":"W2384015905","doi":"","title":"A New Multiphase Image Segmentation Model by Piecewise Constant Level Set Method","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Piecewise; Computer science; Constant (computer programming); Segmentation; Noise (video); Algorithm; Convergence (economics); Image segmentation; Function (biology); Rate of convergence; Image (mathematics); Minification; Set (abstract data type); Mathematical optimization; Level set method; Level set (data structures); Artificial intelligence; Mathematics; Key (lock)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005159948,0.0004986285,0.0006908218,0.0009585958,0.000350816,0.001077446,0.001774843,0.001089023,0.001450713],"category_scores_gemma":[0.0009107396,0.0005667385,0.001229369,0.001014691,0.0005387617,0.001792107,0.0007360749,0.001138404,0.0005299152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009167953,"about_ca_system_score_gemma":0.001058628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003848067,"about_ca_topic_score_gemma":0.002436284,"domain_scores_codex":[0.9995686,0.00007776069,0.0000232844,0.00008897454,0.0002033032,0.0000381209],"domain_scores_gemma":[0.9998003,0.00005529414,0.00002623686,0.00002632278,0.00007288477,0.0000189183],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001236168,0.00005550536,0.0008928851,0.0003023561,0.0001107598,0.0002156088,0.000243861,0.6796821,0.03432291,0.07390317,0.003669499,0.2064776],"study_design_scores_gemma":[0.000005971725,0.00002074109,0.00009429073,0.000005960419,0.00001246094,0.00007085613,0.000007135682,0.991174,0.002255996,0.003934328,0.0024048,0.00001347622],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002483299,0.0001550621,0.9963821,0.0001082324,0.00002640172,0.00001558632,0.00002287344,0.000164469,0.0006417876],"genre_scores_gemma":[0.2408698,0.001218291,0.7499624,0.0001934262,0.0001150842,0.0002051625,0.0002626189,0.0002642862,0.006908894],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003848067,"threshold_uncertainty_score":0.007651329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04333500166671185,"score_gpt":0.3361468029685669,"score_spread":0.2928118013018551,"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."}}