{"id":"W2128089874","doi":"10.1109/tpami.2002.1114849","title":"Flux maximizing geometric flows","year":2002,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":381,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; IBM (Canada)","funders":"","keywords":"Image segmentation; Surface (topology); Artificial intelligence; Segmentation; Computer science; Computer vision; Image (mathematics); Set (abstract data type); Field (mathematics); Level set (data structures); Algorithm; Mathematics; Geometry","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":[0.002121588,0.001675106,0.001609133,0.001941378,0.0007044073,0.001622569,0.0008778304,0.00238986,0.003338839],"category_scores_gemma":[0.005575524,0.0007454673,0.001184464,0.0009473988,0.001718612,0.002410419,0.001411401,0.0008401353,0.0007140712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001891595,"about_ca_system_score_gemma":0.0008935363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007297877,"about_ca_topic_score_gemma":0.0005236481,"domain_scores_codex":[0.9996246,0.0001565325,0.00002143633,0.00006684971,0.0000875393,0.00004299671],"domain_scores_gemma":[0.9989384,0.0006107214,0.0001305033,0.00005539029,0.0001954381,0.00006964009],"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":[0.000183261,0.00006115178,0.0005946065,0.0003690313,0.00004666,0.0001159711,0.0001872654,0.5076425,0.01413665,0.368609,0.003357901,0.1046959],"study_design_scores_gemma":[0.00003121506,0.00008811017,0.0002139896,0.00003318478,0.00001806499,0.0001038788,0.00001540352,0.9097753,0.003310858,0.08312738,0.003257726,0.00002499945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01599214,0.0008454071,0.976333,0.0004222174,0.00007163605,0.0001001891,0.00007655391,0.0002410012,0.005917876],"genre_scores_gemma":[0.4447431,0.002130844,0.5388593,0.00029115,0.0002758763,0.0005888889,0.0003205883,0.000454977,0.01233529],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003338839,"threshold_uncertainty_score":0.01372457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03171995903845451,"score_gpt":0.2775371475731147,"score_spread":0.2458171885346601,"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."}}