{"id":"W2050424943","doi":"10.1142/s0219467812500040","title":"CONTOUR INTERPOLATION USING LEVEL-SET ANALYSIS","year":2012,"lang":"en","type":"article","venue":"International Journal of Image and Graphics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Interpolation (computer graphics); Computer vision; Artificial intelligence; Curvature; Computer science; Visualization; Tracing; Contour line; Set (abstract data type); Boundary (topology); Trajectory; Motion (physics); Mathematics; Geometry; Geography; Cartography","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":[0.001334116,0.0007978879,0.0009945281,0.001978873,0.0004874095,0.00154592,0.002145893,0.001343923,0.002968014],"category_scores_gemma":[0.002798284,0.0009775064,0.001823477,0.001171759,0.0007919295,0.001533635,0.00146145,0.001960031,0.001188911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008913637,"about_ca_system_score_gemma":0.001001709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00165501,"about_ca_topic_score_gemma":0.001499893,"domain_scores_codex":[0.9992803,0.0001135075,0.00004281404,0.0001262818,0.000383065,0.00005413286],"domain_scores_gemma":[0.9990369,0.0004172864,0.000101457,0.0002049634,0.0001966249,0.00004282497],"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":[0.0001453926,0.00009251104,0.001016091,0.0002165537,0.00009658455,0.0001401619,0.0002520336,0.5824287,0.03767632,0.04352193,0.001732282,0.3326815],"study_design_scores_gemma":[0.000005986054,0.00002306295,0.000080602,0.000008429582,0.000007627893,0.00002809716,0.00000494978,0.9858251,0.005609471,0.006888622,0.001507318,0.00001078329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001676626,0.00002569937,0.99767,0.00001833401,0.00000859465,0.00002183541,0.00001238193,0.0003036238,0.0002629134],"genre_scores_gemma":[0.09865544,0.0001144376,0.8997045,0.00003888676,0.00002206415,0.0001145849,0.0001471598,0.0002891609,0.0009137715],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002968014,"threshold_uncertainty_score":0.009929001,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05400067830766171,"score_gpt":0.3627044970823357,"score_spread":0.308703818774674,"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."}}