{"id":"W2065814531","doi":"10.1118/1.2031005","title":"Po‐Poster ‐ 26: Investigation of normalized mutual information for co‐registration of CT — MR images of permanent prostate implants","year":2005,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Cancer Foundation","funders":"","keywords":"Imaging phantom; Image registration; Prostate; Mutual information; Dosimetry; Image fusion; Medicine; Computer vision; Data set; Medical imaging; Computer science; Nuclear medicine; Artificial intelligence; Visualization; Image quality; Fiducial marker; Image (mathematics)","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":[0.002527529,0.0004441553,0.0004008596,0.001258591,0.0002395916,0.0008515153,0.0005838196,0.0005013329,0.001644661],"category_scores_gemma":[0.007959773,0.0003017736,0.0006851174,0.0006794066,0.0005404011,0.0008903667,0.0006149052,0.0003223449,0.0004089439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004148615,"about_ca_system_score_gemma":0.0005515958,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006274993,"about_ca_topic_score_gemma":0.0006197013,"domain_scores_codex":[0.9989391,0.0004051565,0.00007355493,0.000130164,0.0003944404,0.00005755987],"domain_scores_gemma":[0.9969921,0.001737428,0.000353492,0.0003262617,0.0005141046,0.00007654575],"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":[0.002721327,0.0003118281,0.01159326,0.0007227322,0.0003712481,0.000287281,0.0003970933,0.1939242,0.3074814,0.00670468,0.001576707,0.4739082],"study_design_scores_gemma":[0.00002712274,0.0006720101,0.01294154,0.00002585441,0.0001256331,0.0008249631,0.00006998234,0.7312925,0.2495488,0.001841673,0.002559418,0.00007047836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3613225,0.001106876,0.6316268,0.0002060663,0.00005832385,0.0002011433,0.0002115728,0.00107482,0.004191831],"genre_scores_gemma":[0.8324397,0.0002581946,0.1653078,0.00002417197,0.00002105243,0.00007494447,0.000278381,0.0002274608,0.001368386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002527529,"threshold_uncertainty_score":0.01336706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01727211839381827,"score_gpt":0.2957368246190725,"score_spread":0.2784647062252542,"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."}}