{"id":"W2482109113","doi":"","title":"Method for semi-automated image segmentation of blood vessels in MRI images","year":2013,"lang":"en","type":"article","venue":"Journal of undergraduate research in Alberta","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Segmentation; Computer science; Computer vision; Artificial intelligence; Process (computing); Image quality; MATLAB; Image segmentation; Image processing; Medical imaging; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001008564,0.001432964,0.0009956135,0.003127324,0.0006402758,0.001710125,0.001620578,0.001345193,0.01310639],"category_scores_gemma":[0.002536849,0.0007548195,0.001315221,0.001503553,0.0005247068,0.0007629902,0.001136191,0.0009978313,0.007924277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005212625,"about_ca_system_score_gemma":0.001495369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002112244,"about_ca_topic_score_gemma":0.002602516,"domain_scores_codex":[0.9987123,0.0001866014,0.0001251928,0.0003449277,0.0005378425,0.00009303935],"domain_scores_gemma":[0.9989485,0.0003778244,0.0001235547,0.0001304745,0.0003779155,0.00004162089],"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":[0.000271268,0.0000976091,0.001178453,0.0008975834,0.0001457949,0.0002981232,0.0003196707,0.01670826,0.09476833,0.004750879,0.0136929,0.8668711],"study_design_scores_gemma":[0.0001153795,0.0002795602,0.005784196,0.0002351076,0.0001475943,0.002379361,0.0001881399,0.7340097,0.1728884,0.007771092,0.0760408,0.0001606282],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002356593,0.0001412842,0.9895992,0.00004684103,0.00003657925,0.0001912398,0.0002182907,0.006677874,0.0007320541],"genre_scores_gemma":[0.01945345,0.0001558088,0.9772439,0.00003350394,0.00002184678,0.0003477648,0.0004697227,0.0005613404,0.001712628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01310639,"threshold_uncertainty_score":0.0438453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02837756281508283,"score_gpt":0.3870216535412288,"score_spread":0.3586440907261459,"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."}}