{"id":"W2162930331","doi":"10.1109/iembs.2006.260614","title":"Towards an Automatic Coronary Artery Segmentation Algorithm","year":2006,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Filter (signal processing); Computer vision; Computer science; Algorithm; Artificial intelligence; Fluoroscopy; Frame (networking); Segmentation; Image segmentation; Image (mathematics); Image processing; Feature extraction; Pattern recognition (psychology); Medicine; Radiology","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":[0.0009650458,0.000767684,0.000835222,0.001612547,0.0007702645,0.001442276,0.00144952,0.001593411,0.003387894],"category_scores_gemma":[0.001959913,0.0006826518,0.0006528837,0.001076736,0.000612535,0.0008321199,0.0008286352,0.001303599,0.002256409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006913409,"about_ca_system_score_gemma":0.001708867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002561214,"about_ca_topic_score_gemma":0.002978888,"domain_scores_codex":[0.9991031,0.0001331548,0.0000489129,0.000243644,0.0004136485,0.00005755702],"domain_scores_gemma":[0.9991066,0.0002478452,0.00007630571,0.00009581638,0.0004325271,0.0000408071],"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":[0.0001365005,0.00006947343,0.0006387062,0.0001579831,0.00006377776,0.0001147049,0.0001003895,0.04860394,0.1008234,0.01276045,0.007064428,0.8294663],"study_design_scores_gemma":[0.00009055001,0.0001497065,0.001616556,0.00005714121,0.00005318893,0.0005873803,0.00005054548,0.8996986,0.05234506,0.01320967,0.03207688,0.00006468103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001725279,0.00009090312,0.9967687,0.00004773389,0.00001912702,0.00002810142,0.00002275505,0.0009988958,0.0002984703],"genre_scores_gemma":[0.01152245,0.00009902792,0.9870755,0.00004080658,0.00002575654,0.00006104507,0.00009547375,0.00009906815,0.0009808651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003387894,"threshold_uncertainty_score":0.01133364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01275755624975541,"score_gpt":0.2804792619820088,"score_spread":0.2677217057322535,"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."}}