{"id":"W2099275521","doi":"10.1016/j.media.2008.02.003","title":"A geometric flow for segmenting vasculature in proton-density weighted MRI","year":2008,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Magnetic resonance imaging; Computer vision; Pattern recognition (psychology); Radiology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003770475,0.0001324884,0.0005017757,0.0006539368,0.0001193433,0.000008317023,0.0001135904,0.0001593986,0.0003243708],"category_scores_gemma":[0.0004831444,0.0001059531,0.0003888585,0.003822479,0.0001088084,0.00007555522,0.00005334972,0.0003054509,0.00001060623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006992905,"about_ca_system_score_gemma":0.00007271973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005688409,"about_ca_topic_score_gemma":0.00002798244,"domain_scores_codex":[0.9984749,0.00002212531,0.0003518213,0.0003634965,0.0005007926,0.0002868942],"domain_scores_gemma":[0.9990405,0.000099145,0.0000787664,0.0003730886,0.000183244,0.0002252514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007840387,0.008408065,0.6634592,0.001390693,0.005929004,0.00396824,0.001633509,0.0005060558,0.01768235,0.0008246341,0.04376242,0.2516518],"study_design_scores_gemma":[0.006431822,0.0002972384,0.1155763,0.0002372571,0.005064771,0.0002523696,0.0001475677,0.8097348,0.02401131,0.001761583,0.03563586,0.0008491714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06478257,0.0002213598,0.9311484,0.002252431,0.000009051963,0.001093603,0.00000839406,0.0001226023,0.000361585],"genre_scores_gemma":[0.2956114,0.0009014296,0.699492,0.001209315,0.0002429407,0.00110347,0.0003899531,0.00003476244,0.001014806],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8092287,"threshold_uncertainty_score":0.4320644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01318205857463946,"score_gpt":0.3154482726148905,"score_spread":0.3022662140402511,"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."}}