{"id":"W2082214883","doi":"10.1088/0031-9155/49/21/007","title":"Prostate segmentation algorithm using dyadic wavelet transform and discrete dynamic contour","year":2004,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; University of Waterloo","funders":"Canada Research Chairs","keywords":"Artificial intelligence; Computer science; Segmentation; Discrete wavelet transform; Computer vision; Wavelet; Pixel; Interpolation (computer graphics); Smoothing; Active contour model; Algorithm; Pattern recognition (psychology); Wavelet transform; Mathematics; Image segmentation; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002243691,0.0001014684,0.0001894019,0.00006698471,0.00004646656,0.00001345936,0.0001039785,0.00004281804,0.000002443584],"category_scores_gemma":[0.00001543298,0.00007471358,0.00001030765,0.000161826,0.0002703299,0.0002212707,0.00004147882,0.0001174488,4.370625e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004247277,"about_ca_system_score_gemma":0.00002811738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001956919,"about_ca_topic_score_gemma":0.00001541682,"domain_scores_codex":[0.999268,0.00004285229,0.0002017483,0.0002428485,0.00007826065,0.0001662974],"domain_scores_gemma":[0.9997126,0.0000468635,0.00005890075,0.0000944096,0.00002493087,0.00006227363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003734966,0.00001967442,0.0001140678,0.00002544232,0.000008695705,0.000008425559,0.001770159,0.000003357088,0.04296537,0.003559274,0.00000856694,0.9515132],"study_design_scores_gemma":[0.008945946,0.001967193,0.001820965,0.0005193108,0.00005626058,0.0001369644,0.001543981,0.2372338,0.06987546,0.6770937,0.0001336925,0.000672731],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02460635,0.000256626,0.9726858,0.002039157,0.00007650297,0.0002355645,0.000003600587,0.00004125164,0.00005513671],"genre_scores_gemma":[0.4901301,0.001194139,0.5066268,0.001845979,0.0001068606,0.00002777053,0.00004750107,0.000009044335,0.00001184122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9508405,"threshold_uncertainty_score":0.3046731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08783069299977876,"score_gpt":0.3933152395601233,"score_spread":0.3054845465603446,"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."}}