{"id":"W15394667","doi":"10.1007/978-3-642-11840-1_12","title":"SRAD, Optical Flow and Primitive Prior Based Active Contours for Echocardiography","year":2010,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Vector flow; Active contour model; Speckle pattern; Artificial intelligence; Computer vision; Computer science; Optical flow; Speckle noise; Sensitivity (control systems); Flow (mathematics); Pattern recognition (psychology); Image (mathematics); Image segmentation; Mathematics; Geometry; Engineering","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":[0.0003975825,0.0007813713,0.0007752111,0.0009829758,0.0002030176,0.001094884,0.0009456,0.001000541,0.005744366],"category_scores_gemma":[0.001112485,0.0006086395,0.0005696988,0.001428836,0.0006546695,0.001340355,0.0006852337,0.001852984,0.003947767],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003145321,"about_ca_system_score_gemma":0.0005047885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036572,"about_ca_topic_score_gemma":0.001496295,"domain_scores_codex":[0.9997591,0.00003891764,0.00001393898,0.0000515904,0.0001266566,0.000009665404],"domain_scores_gemma":[0.9997163,0.0001270477,0.00001979579,0.00004985743,0.0000720462,0.00001488037],"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":[0.000073868,0.00003683907,0.00009014111,0.0002903462,0.00002448798,0.00007644109,0.00007762428,0.03190213,0.01526703,0.07363252,0.0253401,0.8531884],"study_design_scores_gemma":[0.00003229648,0.0001018681,0.0005833553,0.0001221992,0.00004176208,0.0009242709,0.00003571405,0.6593751,0.01727879,0.1265588,0.1948601,0.00008573756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007801745,0.004272815,0.989843,0.000141668,0.0002520226,0.00002307342,0.00009085751,0.0008937859,0.003702778],"genre_scores_gemma":[0.02777819,0.009011919,0.9325807,0.0001519843,0.0004457603,0.00008354058,0.0005232109,0.0005173888,0.02890733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005744366,"threshold_uncertainty_score":0.01921684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0256826653310869,"score_gpt":0.3050042711786938,"score_spread":0.2793216058476069,"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."}}