{"id":"W2617022775","doi":"","title":"Assessing accuracy of automated segmentation methods for brain lateral ventricles in MRI data","year":2015,"lang":"en","type":"article","venue":"Undergraduate Research Journal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Lateral ventricles; Computer vision; Pattern recognition (psychology); Magnetic resonance imaging; Hausdorff distance; Sørensen–Dice coefficient; Image segmentation; Anatomy; Radiology; 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":[0.01677998,0.001471181,0.001284918,0.006481876,0.001075373,0.002414676,0.001440732,0.002360021,0.0008222144],"category_scores_gemma":[0.05238442,0.0005498247,0.001502779,0.002336539,0.001193498,0.001795455,0.00135558,0.0008314363,0.000630661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001165647,"about_ca_system_score_gemma":0.001453726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009109792,"about_ca_topic_score_gemma":0.009501887,"domain_scores_codex":[0.9890413,0.003316059,0.001394819,0.002360396,0.003411326,0.0004761907],"domain_scores_gemma":[0.9547439,0.03078805,0.003970009,0.003422835,0.0066397,0.000435584],"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":[0.003913308,0.0007363972,0.1634459,0.001704471,0.002747305,0.0005510881,0.001847556,0.297498,0.04312422,0.002571439,0.005027709,0.4768327],"study_design_scores_gemma":[0.0001135222,0.0009649603,0.09473738,0.0001959957,0.0003043294,0.000707682,0.0004332301,0.8617709,0.03511041,0.002038254,0.003430574,0.0001928888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.837047,0.004459446,0.1493126,0.0003122814,0.0003173717,0.0004279861,0.001497527,0.003862197,0.00276358],"genre_scores_gemma":[0.8573827,0.0007069232,0.1372132,0.0001235819,0.00008290808,0.0002143701,0.003086873,0.0004705415,0.0007189285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01677998,"threshold_uncertainty_score":0.08874202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3438230343102174,"score_gpt":0.5873624278722211,"score_spread":0.2435393935620037,"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."}}