{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01780333,0.0001033939,0.0002078438,0.0007383138,0.0001597533,0.0008133638,0.001694395,0.00005923007,0.000006658589],"category_scores_gemma":[0.006665744,0.00008948636,0.00003907835,0.001036363,0.0001313052,0.003543247,0.0006245942,0.0004760821,0.000003671689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000273518,"about_ca_system_score_gemma":0.0008195766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000491143,"about_ca_topic_score_gemma":0.000004921487,"domain_scores_codex":[0.9952526,0.002338892,0.0006141565,0.0003227261,0.000998835,0.0004727755],"domain_scores_gemma":[0.9957728,0.002429862,0.0002829964,0.0005333999,0.0006851582,0.0002958272],"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":[0.00004479731,0.0002605972,0.0005921538,0.00008483563,0.00004892134,0.0000753757,0.00105139,0.0001314751,0.0606815,0.001923772,0.06587451,0.8692307],"study_design_scores_gemma":[0.002187432,0.0002954242,0.0005635328,0.0002249581,0.000005870675,0.000147589,0.0005213442,0.7551472,0.123982,0.1159133,0.0008340716,0.0001771991],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002120464,0.0001693183,0.9818685,0.01511691,0.0001515387,0.0003846911,0.000003685725,0.0001218107,0.0000630679],"genre_scores_gemma":[0.0607097,0.0001131987,0.9389028,0.0001215124,0.00006317308,0.00001648564,0.00002436581,0.00001217105,0.0000365994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8690535,"threshold_uncertainty_score":0.798,"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."}}