{"id":"W1994746045","doi":"10.1109/iembs.2007.4352415","title":"Three Different Strategies for Real-Time Prostate Capsule Volume Computation from 3-D End-Fire Transrectal Ultrasound","year":2007,"lang":"en","type":"article","venue":"Conference proceedings","topic":"Prostate Cancer Diagnosis and Treatment","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Initialization; Computer science; Artificial intelligence; Ground truth; Prostate biopsy; Computer vision; Segmentation; Image segmentation; Volume (thermodynamics); Edge detection; Imaging phantom; Ultrasound; Enhanced Data Rates for GSM Evolution; Data set; Prostate; Pattern recognition (psychology); Image processing; Nuclear medicine; Image (mathematics); Medicine; Radiology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001643704,0.0003376282,0.0004823923,0.00007305458,0.0001537055,0.0002065095,0.0001039589,0.0001298298,0.0002208664],"category_scores_gemma":[0.00003445976,0.0002789043,0.0001333573,0.000125113,0.0001377646,0.0003009327,0.0000181857,0.0001514099,0.00003075657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002214019,"about_ca_system_score_gemma":0.0002272133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004461627,"about_ca_topic_score_gemma":0.0001133972,"domain_scores_codex":[0.9982105,0.000003215503,0.000410187,0.0005546349,0.0003046369,0.0005168314],"domain_scores_gemma":[0.998899,0.0001035145,0.0001655164,0.0001014137,0.0004869929,0.0002435179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002384342,0.001544193,0.1647202,0.0009556574,0.000784609,0.0000559183,0.02027618,0.000006497725,0.6606184,0.006088279,0.007584924,0.1349808],"study_design_scores_gemma":[0.01072762,0.004593537,0.7537625,0.001181296,0.0009610086,0.00006483147,0.006773372,0.00999485,0.1751271,0.03420971,0.001424993,0.001179164],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850108,0.0002092419,0.009351723,0.0006158807,0.0001407488,0.001728055,0.0002286575,0.0002077764,0.002507119],"genre_scores_gemma":[0.9959317,0.0002590911,0.002452722,0.00008131527,0.0002058238,0.0002948218,0.0005121278,0.00004509028,0.000217237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5890424,"threshold_uncertainty_score":0.9999663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224956051989313,"score_gpt":0.2737770614026658,"score_spread":0.2515275008827727,"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."}}