{"id":"W2009298542","doi":"10.1109/embc.2012.6345963","title":"Automatic brain tumor extraction from T1-weighted coronal MRI using fast bounding box and dynamic snake","year":2012,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Massachusetts General Hospital","keywords":"Minimum bounding box; Computer science; Preprocessor; Artificial intelligence; Segmentation; Hausdorff distance; Pattern recognition (psychology); Computer vision; Voxel; Coronal plane; Image segmentation; Bounding overwatch; 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":[0.0007994914,0.0007549271,0.0008428026,0.001971594,0.0003583866,0.0007464781,0.0007041489,0.001190629,0.0009841472],"category_scores_gemma":[0.001466278,0.0007719818,0.0009401185,0.001081024,0.0003988865,0.001138471,0.0007212929,0.000674761,0.0007772864],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002551618,"about_ca_system_score_gemma":0.0005849997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008592448,"about_ca_topic_score_gemma":0.001074769,"domain_scores_codex":[0.9996575,0.00005912593,0.00002945415,0.00008213668,0.0001404363,0.00003141903],"domain_scores_gemma":[0.9994853,0.0002260159,0.00007832205,0.00008118621,0.0001031502,0.00002609743],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001833705,0.0000628597,0.001507053,0.0002787679,0.00009754433,0.0004888663,0.0002157225,0.03813868,0.4373444,0.004210876,0.002075434,0.5153965],"study_design_scores_gemma":[0.00003103098,0.0001836367,0.005983966,0.00007544143,0.0001046124,0.003390083,0.00009498442,0.7314212,0.2397052,0.0077557,0.0111214,0.0001327718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01813994,0.0004353916,0.9797235,0.00007226286,0.00001534483,0.00004325857,0.00006085572,0.001068743,0.0004407034],"genre_scores_gemma":[0.08581241,0.0006972756,0.911712,0.00005097392,0.00002055996,0.00007624266,0.0002950269,0.0003064226,0.001029172],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001971594,"threshold_uncertainty_score":0.004228175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01836252305819018,"score_gpt":0.3137670481151659,"score_spread":0.2954045250569757,"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."}}