{"id":"W2063932100","doi":"10.1117/12.844476","title":"Design of a predictive targeting error simulator for MRI-guided prostate biopsy","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Cancer Institute","keywords":"Contouring; Computer science; Prostate biopsy; Segmentation; Computer vision; Rendering (computer graphics); Workflow; Simulation; Artificial intelligence; Prostate cancer; Computer graphics (images); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006969954,0.0005182402,0.0004929058,0.0003080789,0.0002717281,0.000720655,0.001872774,0.0008691102,0.002390327],"category_scores_gemma":[0.002630299,0.0004442787,0.0004553949,0.0002914123,0.000412979,0.000391364,0.0006828243,0.0006528847,0.0004001613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008390849,"about_ca_system_score_gemma":0.001424746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004034447,"about_ca_topic_score_gemma":0.002396373,"domain_scores_codex":[0.9997583,0.00005406907,0.00001569896,0.00003171236,0.0001172337,0.00002291801],"domain_scores_gemma":[0.9991664,0.0004515742,0.00008800674,0.00006444215,0.000171567,0.00005808719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009193667,0.00005263543,0.00069574,0.00004605577,0.00001587917,0.00005804841,0.00006692039,0.9820324,0.006628793,0.001685409,0.0003672557,0.008258975],"study_design_scores_gemma":[0.00001176062,0.00001935646,0.00006417714,0.000002646513,0.000003593787,0.00001024068,0.000004633237,0.9967557,0.002360702,0.0001997504,0.0005617713,0.0000056663],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05373671,0.00007889934,0.939711,0.0001298873,0.00005311386,0.0002658963,0.0002106749,0.003304352,0.002509441],"genre_scores_gemma":[0.6403112,0.000185622,0.3543606,0.0001112952,0.00001723916,0.0006064636,0.0004395182,0.0006809501,0.003287097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004034447,"threshold_uncertainty_score":0.008021951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01576482775359769,"score_gpt":0.2643082477467352,"score_spread":0.2485434199931375,"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."}}