{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001399443,0.001127027,0.0009657752,0.003220651,0.0007364359,0.00164667,0.001279959,0.001642731,0.002863473],"category_scores_gemma":[0.003025314,0.0007260477,0.001133174,0.001607378,0.0008453111,0.001571762,0.001162885,0.001090545,0.001164867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007175304,"about_ca_system_score_gemma":0.001834149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004894244,"about_ca_topic_score_gemma":0.003405859,"domain_scores_codex":[0.9997075,0.00006927471,0.00002575726,0.00006515886,0.0001013168,0.00003092704],"domain_scores_gemma":[0.9992616,0.0003205926,0.00006879406,0.00007581316,0.0002183795,0.00005485023],"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":[0.000288279,0.0001379798,0.001535186,0.000329657,0.00006775785,0.0002121895,0.0001476267,0.0604593,0.04998422,0.02153805,0.005400334,0.8598995],"study_design_scores_gemma":[0.00006253702,0.0002253301,0.001242792,0.00005321574,0.00009131396,0.0006954041,0.00004723582,0.9461886,0.02678106,0.016037,0.008515892,0.00005953813],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003021668,0.0001623148,0.995676,0.00008202118,0.00003320979,0.00008023968,0.00006929565,0.0006882084,0.0001870389],"genre_scores_gemma":[0.02388642,0.0004634224,0.9743533,0.00005545553,0.00008834605,0.0001410866,0.000176355,0.0002098972,0.0006257195],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004894244,"threshold_uncertainty_score":0.009731531,"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."}}