{"id":"W2130541310","doi":"10.1109/icip.2007.4379878","title":"Segmentation of Medical Ultrasound Images using Active Contours","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Image segmentation; Segmentation; Medical ultrasound; Ultrasound; Active contour model; Medical imaging; Radiology; Medicine","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":[0.0007853089,0.0008768197,0.0009262012,0.001905707,0.0003540654,0.001205698,0.0008284248,0.001752599,0.001045141],"category_scores_gemma":[0.002007349,0.0007126895,0.0009848004,0.001237022,0.000912209,0.0009484663,0.0007087381,0.0007087333,0.0006574707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004653028,"about_ca_system_score_gemma":0.0005603424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001114212,"about_ca_topic_score_gemma":0.001006818,"domain_scores_codex":[0.9993737,0.0001547895,0.00005607924,0.0001272863,0.0002525454,0.00003559035],"domain_scores_gemma":[0.9994251,0.0002742291,0.00008842448,0.00009875118,0.00009284302,0.00002073367],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003998605,0.0001179358,0.0007177371,0.000467631,0.000137963,0.0004509203,0.0003835952,0.1597106,0.3713435,0.01305438,0.003051667,0.4501641],"study_design_scores_gemma":[0.00004410511,0.0001901132,0.001764159,0.00007179346,0.00006218968,0.0006428718,0.00006687509,0.8311003,0.1405048,0.01233897,0.01313205,0.00008177908],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0104951,0.0005571138,0.9871399,0.0001211226,0.00003372593,0.0000925601,0.00004909538,0.0007976711,0.0007136945],"genre_scores_gemma":[0.1478649,0.001097142,0.8486686,0.000105545,0.00006273344,0.0001653925,0.0002755708,0.0001829287,0.00157711],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001905707,"threshold_uncertainty_score":0.004153132,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02434074534883581,"score_gpt":0.3596687221142167,"score_spread":0.3353279767653809,"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."}}