{"id":"W2100646820","doi":"10.1109/icassp.2004.1326595","title":"Segmentation of prostate contours from ultrasound images","year":2004,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; Queen's University","funders":"Nanyang Technological University; University of Pennsylvania","keywords":"Computer vision; Artificial intelligence; Image segmentation; Segmentation; Computer science; Ultrasound; Prostate; Radiology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001263122,0.00007071179,0.00009665787,0.00004950557,0.00002562157,0.00005931526,0.000312604,0.00002437226,0.0001234988],"category_scores_gemma":[0.00006133933,0.00005999846,0.00002814707,0.0001455467,0.00007145159,0.0005918599,0.00004731423,0.00004676355,0.00002603423],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003696935,"about_ca_system_score_gemma":0.00004877933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000507,"about_ca_topic_score_gemma":0.0000100226,"domain_scores_codex":[0.9991841,0.00002855151,0.0002180433,0.0001880982,0.0002723521,0.0001088523],"domain_scores_gemma":[0.9994386,0.000090462,0.00009798937,0.0002349423,0.00007622629,0.00006182342],"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":[0.00000378274,0.0000861791,0.0008403904,0.000008412392,0.00001550128,0.000006263022,0.001324074,0.00001622421,0.9595762,0.003704174,0.001691011,0.03272776],"study_design_scores_gemma":[0.0004453433,0.00005597086,0.002342501,0.00001539852,0.000003085327,0.000001881656,0.000124386,0.00001730456,0.982592,0.01432526,0.000008047441,0.00006875394],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01630351,0.00003426335,0.9811732,0.0004359782,0.00007489326,0.000195095,0.000007752588,0.000228469,0.001546843],"genre_scores_gemma":[0.2696398,0.00002825719,0.7296131,0.0005088791,0.00001359022,0.00001437697,0.00001384518,0.000003916854,0.0001643055],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2533363,"threshold_uncertainty_score":0.2446666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009691389681206197,"score_gpt":0.2700350276894697,"score_spread":0.2603436380082635,"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."}}