{"id":"W2100217050","doi":"10.1109/iembs.2005.1616162","title":"3D TRUS Image Segmentation in Prostate Brachytherapy","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials","funders":"","keywords":"Brachytherapy; Imaging phantom; Prostate; Prostate brachytherapy; Ultrasound; Prostate cancer; Medicine; Radiation treatment planning; Radiology; 3D ultrasound; Image segmentation; Segmentation; Nuclear medicine; Computer science; Artificial intelligence; Radiation therapy; Cancer","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":[0.0002833116,0.00008398009,0.00008464916,0.0001236625,0.00002831015,0.0001152188,0.0003006794,0.00002250239,0.0003469109],"category_scores_gemma":[0.00001864863,0.00007371359,0.00001984597,0.0002931033,0.0000325143,0.001311,0.00005344155,0.00008541786,0.0001505723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007313664,"about_ca_system_score_gemma":0.00003054222,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003571903,"about_ca_topic_score_gemma":0.00002566627,"domain_scores_codex":[0.9990302,0.00005994197,0.0002458005,0.0002396045,0.000242405,0.0001820334],"domain_scores_gemma":[0.9995802,0.00003508368,0.00005334294,0.000233612,0.00003420727,0.00006356072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003772918,0.00007609552,0.0002625976,0.000004629469,0.000002191741,0.00001542494,0.0009242611,0.000006975742,0.04224383,0.0009291135,0.002181578,0.9533495],"study_design_scores_gemma":[0.001377639,0.0001370956,0.002980703,0.00001926303,0.00000146009,0.00002566469,0.00008312988,0.0517317,0.9396747,0.001875741,0.001809652,0.0002832481],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007576574,0.00003037178,0.9848,0.002205243,0.00004927873,0.0003013785,5.260349e-7,0.0003694269,0.004667227],"genre_scores_gemma":[0.03292928,0.00005403237,0.9626849,0.003140957,0.00003280362,0.00006000084,0.000004216874,0.000006578197,0.001087192],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9530663,"threshold_uncertainty_score":0.3798431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008721355646703953,"score_gpt":0.2901038038119102,"score_spread":0.2813824481652062,"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."}}