{"id":"W2153569953","doi":"10.1109/crv.2008.15","title":"Prostate Segmentation from 2-D Ultrasound Images Using Graph Cuts and Domain Knowledge","year":2008,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Initialization; Computer science; Robustness (evolution); Cut; Domain knowledge; Graph; Prior probability; Artificial intelligence; Segmentation; Inference; Image segmentation; Pattern recognition (psychology); Computer vision; Algorithm; Theoretical computer science; Bayesian probability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001629402,0.0001223688,0.0001195056,0.0001081848,0.0001790938,0.0001174038,0.000230877,0.00003825359,0.00006567345],"category_scores_gemma":[0.00003162882,0.0001072465,0.00002650059,0.0002657305,0.0001690626,0.0008886514,0.0001100362,0.00007854156,0.00001886153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003452528,"about_ca_system_score_gemma":0.00004677708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001794238,"about_ca_topic_score_gemma":0.000009816638,"domain_scores_codex":[0.9989468,0.00009402532,0.0002195063,0.0003411272,0.0002198933,0.0001785995],"domain_scores_gemma":[0.9993583,0.0001495915,0.00007246684,0.0002324466,0.00006578942,0.0001214191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006589353,0.0001314957,0.008198478,0.00001629282,0.00002873926,0.00005236615,0.007452135,0.000001647769,0.9411638,0.0009195639,0.008447883,0.03358104],"study_design_scores_gemma":[0.0007503991,0.00007403827,0.008702282,0.00003375482,0.000008910691,0.0001176124,0.0002347172,0.001090248,0.9715324,0.01699099,0.0001462344,0.000318387],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1436326,0.0001871961,0.854504,0.00009991336,0.00008445694,0.0002168846,0.000003886527,0.0002923757,0.0009786609],"genre_scores_gemma":[0.09342836,0.0001887982,0.90559,0.0003661783,0.00003317334,0.00001570053,0.00001222839,0.00000873016,0.0003568432],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05108594,"threshold_uncertainty_score":0.4373386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02271103250856971,"score_gpt":0.287771797159327,"score_spread":0.2650607646507573,"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."}}