{"id":"W194567576","doi":"10.1007/978-3-642-33415-3_66","title":"Rotational-Slice-Based Prostate Segmentation Using Level Set with Shape Constraint for 3D End-Firing TRUS Guided Biopsy","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"3D ultrasound; Prostate biopsy; Segmentation; Computer science; Prostate; Ultrasound; Artificial intelligence; Image segmentation; Computer vision; 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":[],"consensus_categories":[],"category_scores_codex":[0.001370555,0.0002440788,0.0002047318,0.0004114151,0.0002896471,0.0003754573,0.0008525098,0.00006282097,0.00001677444],"category_scores_gemma":[0.0001930059,0.0002070172,0.00004245654,0.001329915,0.000503784,0.001240363,0.0001872911,0.0001604603,0.000002870672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002707133,"about_ca_system_score_gemma":0.0005616721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003405791,"about_ca_topic_score_gemma":0.000009755748,"domain_scores_codex":[0.9973727,0.00008630616,0.0004161633,0.000657118,0.0008077055,0.0006600149],"domain_scores_gemma":[0.9984033,0.0004298504,0.0002386367,0.0004230868,0.0002980676,0.0002071006],"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":[0.0000165211,0.00008824835,0.004615573,0.00005121688,0.000008223581,0.000007131086,0.002202534,0.03206788,0.04783611,0.0002097735,0.00001039082,0.9128864],"study_design_scores_gemma":[0.0006669539,0.0001151507,0.002349029,0.00009359689,0.000005078334,0.00006858132,0.00000313501,0.7924107,0.2033733,0.000654521,0.000007283446,0.0002526721],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03191798,0.00003399693,0.9660329,0.0003875079,0.0003724929,0.001066276,0.00001305904,0.0001702587,0.000005475903],"genre_scores_gemma":[0.3930768,4.757424e-7,0.6055731,0.001167891,0.00007778228,0.000084819,0.000009844363,0.000008947109,3.88607e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9126337,"threshold_uncertainty_score":0.8441916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06620918864385694,"score_gpt":0.3353622447775685,"score_spread":0.2691530561337115,"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."}}