{"id":"W2004924126","doi":"10.1109/tmi.2008.929098","title":"Segmentation in Ultrasonic<i>B</i>-Mode Images of Healthy Carotid Arteries Using Mixtures of Nakagami Distributions and Stochastic Optimization","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":157,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Centre Hospitalier de l’Université de Montréal","funders":"","keywords":"Nakagami distribution; Carotid arteries; Ultrasonic sensor; Ultrasonic imaging; Image segmentation; Segmentation; Biomedical engineering; Artificial intelligence; Computer vision; Radiology; Computer science; Medicine; Cardiology; Algorithm","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":[0.001343816,0.0005521933,0.0007068867,0.000961856,0.0002613146,0.0008021459,0.0004648897,0.0008139958,0.0002932241],"category_scores_gemma":[0.002889988,0.0006108592,0.0007175024,0.0004957918,0.0008353867,0.0006306346,0.0005948129,0.0004607625,0.0002115113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005001575,"about_ca_system_score_gemma":0.0007906262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002464313,"about_ca_topic_score_gemma":0.002399938,"domain_scores_codex":[0.99959,0.0001719873,0.00002983679,0.00008198214,0.00008735638,0.00003897457],"domain_scores_gemma":[0.9991565,0.0005249881,0.0001455198,0.00006169822,0.00007952828,0.00003169758],"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":[0.0006222865,0.00007832329,0.004245951,0.0001748176,0.0001018999,0.0002548214,0.0002985316,0.7701892,0.08273189,0.008466774,0.0003654928,0.1324701],"study_design_scores_gemma":[0.00001151208,0.00002592993,0.00113308,0.000005867912,0.0000105815,0.00007305064,0.00001526484,0.9858674,0.009465879,0.003169761,0.0002064017,0.00001531172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06685137,0.0001455247,0.932519,0.00006197287,0.000005959154,0.00001643637,0.00001515689,0.0002130577,0.0001715682],"genre_scores_gemma":[0.4861968,0.0003238659,0.5121568,0.00005928534,0.00003081514,0.00007569171,0.0001321485,0.0001219455,0.0009026676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002464313,"threshold_uncertainty_score":0.007106841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01774464821256064,"score_gpt":0.3038225697878452,"score_spread":0.2860779215752845,"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."}}