{"id":"W1998816291","doi":"10.4236/ojrad.2012.21001","title":"Color Fusion of Magnetic Resonance Images Improves Intracranial Volume Measurement in Studies of Aging","year":2012,"lang":"en","type":"article","venue":"Open Journal of Radiology","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre","funders":"Economic and Social Research Council; Biotechnology and Biological Sciences Research Council; Centre for Cognitive Ageing and Cognitive Epidemiology; Medical Research Council; Engineering and Physical Sciences Research Council; Age UK; Mrs Gladys Row Fogo Charitable Trust","keywords":"Thresholding; Segmentation; Magnetic resonance imaging; Artificial intelligence; Medicine; Fusion; Gold standard (test); Computer vision; Nuclear medicine; Computer science; Pattern recognition (psychology); Radiology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00339879,0.00008425415,0.0004545429,0.0001580637,0.00002438142,0.00001502511,0.0009443451,0.00004170154,0.00002958536],"category_scores_gemma":[0.0006100191,0.00006555901,0.00004414781,0.0001822778,0.0002079101,0.0006438565,0.0003328713,0.000156984,0.00000104546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007972849,"about_ca_system_score_gemma":0.0001028924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002658227,"about_ca_topic_score_gemma":0.000004745092,"domain_scores_codex":[0.9982776,0.0003620856,0.0007427372,0.0001076363,0.0003148518,0.0001950722],"domain_scores_gemma":[0.9986381,0.0001333032,0.0006009947,0.0001716333,0.0003876782,0.00006829873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009221076,0.0002180065,0.01272673,0.0000673516,0.00003470714,0.00003916527,0.002605772,0.00000432099,0.5594971,0.0003020837,0.004500484,0.4199121],"study_design_scores_gemma":[0.004925803,0.005755192,0.3089346,0.001144683,0.00007200771,0.001429431,0.001577924,0.000825423,0.6704456,0.002868479,0.00159316,0.0004276939],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6763844,0.05423472,0.2649497,0.002270692,0.001219616,0.0006400854,0.000002858304,0.0000151109,0.0002828359],"genre_scores_gemma":[0.7682624,0.001286706,0.230205,0.000128882,0.00007228385,0.000006079665,1.070144e-7,0.000004453676,0.00003407857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4194844,"threshold_uncertainty_score":0.2673419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0438083665879801,"score_gpt":0.3323624487153748,"score_spread":0.2885540821273948,"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."}}