{"id":"W2103109771","doi":"10.1109/tuffc.2006.1593371","title":"Evaluation of an algorithm for semiautomated segmentation of thin tissue layers in high-frequency ultrasound images","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre; Robarts Clinical Trials; Western University","funders":"","keywords":"Speckle pattern; Ultrasound; Materials science; Segmentation; Biomedical engineering; Algorithm; Contrast (vision); Ultrasonic sensor; Mathematics; Optics; Computer science; Artificial intelligence; Physics; Acoustics; 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":[0.003798479,0.0009182081,0.0006833594,0.000983485,0.0005406461,0.001646278,0.001810845,0.001944887,0.001720385],"category_scores_gemma":[0.007234469,0.0005284257,0.0005980679,0.0007201113,0.0005453133,0.0009277619,0.0006576471,0.0006784823,0.0006459182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009985284,"about_ca_system_score_gemma":0.001876861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003690889,"about_ca_topic_score_gemma":0.003288038,"domain_scores_codex":[0.9983283,0.0004483221,0.0001459321,0.0003126347,0.0006818607,0.00008302771],"domain_scores_gemma":[0.9955952,0.002312768,0.0002139551,0.0003707327,0.001412469,0.00009486598],"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":[0.0009737742,0.0004114703,0.003629182,0.0003779858,0.0002322924,0.0002181787,0.0003413342,0.1533158,0.2157605,0.003737394,0.001668264,0.6193339],"study_design_scores_gemma":[0.00006580104,0.0002351007,0.001750169,0.00001358401,0.00003701785,0.0001950135,0.00003310032,0.943276,0.0526634,0.0004359788,0.001267901,0.00002700253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02269215,0.0000617853,0.9750814,0.00004880214,0.00001855885,0.0001644578,0.00003758112,0.001643757,0.0002515273],"genre_scores_gemma":[0.06605164,0.00004436996,0.9330199,0.00003041368,0.000006082596,0.0002050919,0.0001046901,0.000124582,0.0004133967],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003798479,"threshold_uncertainty_score":0.02008855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009838053996552507,"score_gpt":0.2695139283489612,"score_spread":0.2596758743524086,"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."}}