{"id":"W2137186887","doi":"10.1109/tbme.2009.2012423","title":"Fluid Vector Flow and Applications in Brain Tumor Segmentation","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":142,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Massachusetts General Hospital","keywords":"Computer science; Flow (mathematics); Segmentation; Artificial intelligence; Physics; Mechanics","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":[0.001234106,0.0007447692,0.0005745224,0.002623309,0.0003639122,0.001055154,0.0006625806,0.001471183,0.001933757],"category_scores_gemma":[0.00450333,0.0003388739,0.0004505607,0.002000449,0.001181524,0.001830862,0.0006275037,0.0006409013,0.0005134176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005482357,"about_ca_system_score_gemma":0.0005724535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002560138,"about_ca_topic_score_gemma":0.001088776,"domain_scores_codex":[0.9995821,0.0001455088,0.00002440073,0.00006862168,0.0001539719,0.00002540637],"domain_scores_gemma":[0.9985065,0.001065985,0.00008399915,0.00007423638,0.0002310667,0.00003817376],"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":[0.000137329,0.00006372217,0.001568178,0.000341847,0.0000312501,0.0002677143,0.0002274034,0.3194931,0.02651861,0.04926467,0.003060411,0.5990257],"study_design_scores_gemma":[0.00001437452,0.00005934207,0.0005565757,0.00004726224,0.00001209548,0.0003309058,0.00003421013,0.9344406,0.01447416,0.03745199,0.01254472,0.00003371559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00846207,0.003814206,0.9844106,0.0007622315,0.0001225718,0.00004585353,0.00005134309,0.0006805199,0.001650479],"genre_scores_gemma":[0.3656958,0.008777729,0.6207342,0.0003065909,0.0003832053,0.0001329078,0.0002184454,0.0003089248,0.003442139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002623309,"threshold_uncertainty_score":0.006526589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006624408759743347,"score_gpt":0.2418567824867283,"score_spread":0.2352323737269849,"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."}}