{"id":"W2141548717","doi":"10.1109/tip.2008.2006425","title":"A Region Merging Prior for Variational Level Set Image Segmentation","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; General Electric (Canada)","funders":"","keywords":"Image segmentation; Segmentation; Artificial intelligence; Scale-space segmentation; Computer science; Pattern recognition (psychology); Set (abstract data type); Constant (computer programming); Image (mathematics); Segmentation-based object categorization; Level set (data structures); Mathematics; Computer vision; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003618101,0.0006705661,0.001030909,0.001105106,0.0007249204,0.001028137,0.001770296,0.001902913,0.001413642],"category_scores_gemma":[0.008426717,0.000933135,0.001066196,0.001092051,0.00180775,0.002275891,0.002118962,0.002178786,0.0005621961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053253,"about_ca_system_score_gemma":0.001047198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720281,"about_ca_topic_score_gemma":0.001882996,"domain_scores_codex":[0.9988274,0.0003822934,0.00005317444,0.0002409187,0.0004331051,0.00006310504],"domain_scores_gemma":[0.9973679,0.001600204,0.000275822,0.0003373472,0.0003136306,0.000105124],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002485338,0.00008490585,0.00108587,0.0002164437,0.0001028461,0.0001405267,0.0003070922,0.6020716,0.0465198,0.1683735,0.001569964,0.179279],"study_design_scores_gemma":[0.00001061986,0.00006096697,0.0003677242,0.00002241532,0.00002005036,0.00009176637,0.000009356055,0.9659372,0.008926601,0.02146369,0.003062984,0.00002666937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002986226,0.0001191051,0.9962857,0.00006507098,0.00001013288,0.00002009078,0.00001386521,0.0001194969,0.0003803401],"genre_scores_gemma":[0.1234253,0.0003560576,0.8738017,0.0001451921,0.00009589989,0.0001489804,0.0001313439,0.0003228901,0.001572629],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003618101,"threshold_uncertainty_score":0.01913458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06183671043305122,"score_gpt":0.3236975378288485,"score_spread":0.2618608273957972,"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."}}