{"id":"W2782462533","doi":"","title":"Comparison of Gradient, Gradient Vector Flow and Pressure Force for Image Segmentation Using Active Contours","year":2002,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Vector flow; Pressure gradient; Boundary (topology); Range (aeronautics); Computer vision; Image gradient; Balanced flow; Artificial intelligence; Flow (mathematics); Segmentation; Image segmentation; Pressure-gradient force; Image (mathematics); Computer science; Mathematics; Physics; Mathematical analysis; Geometry; Optics; Mechanics; Materials science; Image texture","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":[0.004217692,0.001234347,0.000857991,0.003128597,0.0003840934,0.00105626,0.0008580335,0.001805635,0.001725824],"category_scores_gemma":[0.01063675,0.000558192,0.0006284404,0.001374868,0.0007136987,0.002233897,0.0007029256,0.0006999365,0.0003899795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006518833,"about_ca_system_score_gemma":0.0007494725,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001748758,"about_ca_topic_score_gemma":0.002190656,"domain_scores_codex":[0.9985159,0.0004838129,0.00009824372,0.0001082191,0.0007135672,0.00008023597],"domain_scores_gemma":[0.9939463,0.00474094,0.0001721304,0.0002108195,0.0007751534,0.0001547116],"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":[0.002249399,0.0002065954,0.001290198,0.0007539582,0.0001850929,0.0001303754,0.000355055,0.09251691,0.09016527,0.007866525,0.001648998,0.8026316],"study_design_scores_gemma":[0.0001859533,0.0007795279,0.002889958,0.00007684037,0.000132861,0.0003111846,0.00006320572,0.8897232,0.09758094,0.004447428,0.003703372,0.0001054713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0618219,0.001851296,0.93213,0.0002367694,0.0001091736,0.0002064871,0.00008034442,0.001877497,0.001686542],"genre_scores_gemma":[0.3214822,0.001232553,0.674572,0.00007432018,0.00006133266,0.0002910888,0.0002768404,0.0005231131,0.001486587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004217692,"threshold_uncertainty_score":0.02230561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05219016867994154,"score_gpt":0.3461653889631144,"score_spread":0.2939752202831729,"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."}}