{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001379255,0.0001175319,0.000215069,0.00008870655,0.00008665555,0.00006054393,0.0002279834,0.00004266839,0.00006174813],"category_scores_gemma":[0.00006240535,0.0001058531,0.0000478431,0.0001400953,0.00007991075,0.0006457649,0.00007383874,0.00006139088,0.000001146349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003721445,"about_ca_system_score_gemma":0.000009249781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000031582,"about_ca_topic_score_gemma":0.00000586274,"domain_scores_codex":[0.9989465,0.00005298773,0.00028829,0.0002836325,0.0002383351,0.000190269],"domain_scores_gemma":[0.9992383,0.0001325486,0.0001885817,0.0002034766,0.0001342062,0.0001028805],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005506696,0.0007035779,0.001353715,0.0004015339,0.0001961536,0.000005861451,0.01702655,0.0001466393,0.620277,0.008478888,0.01415706,0.337198],"study_design_scores_gemma":[0.0004964318,0.0002050777,0.0003151141,0.00002297004,0.00002586139,0.000004123589,0.0002386367,0.5472891,0.4509151,0.0003297277,0.00005701787,0.000100854],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01770207,0.0001482843,0.9807808,0.0001625022,0.000118626,0.0006851004,0.00001496675,0.0001138109,0.0002738733],"genre_scores_gemma":[0.3636099,0.00001214797,0.6359916,0.0001081241,0.00002090903,0.00004023325,0.000006170082,0.000007724941,0.0002031692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5471425,"threshold_uncertainty_score":0.4316564,"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."}}