{"id":"W2133608050","doi":"10.1109/tmi.2006.872142","title":"Intravascular ultrasound image segmentation: a three-dimensional fast-marching method based on gray level distributions","year":2006,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":145,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute","funders":"","keywords":"Intravascular ultrasound; Fast marching method; Segmentation; Computer science; Artificial intelligence; Image segmentation; Computer vision; Ultrasound; Biomedical engineering; Radiology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005541081,0.0002768382,0.0003132411,0.0002812651,0.0004473405,0.00006363809,0.0001286967,0.00009621066,0.002690858],"category_scores_gemma":[0.0001475872,0.0002489692,0.0004211541,0.0003610626,0.0002268651,0.0001437419,0.00000251169,0.00076742,0.0001003849],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002220852,"about_ca_system_score_gemma":0.0002364714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005282898,"about_ca_topic_score_gemma":0.0002458508,"domain_scores_codex":[0.997392,0.0001513325,0.0005246451,0.0004562396,0.001075221,0.0004005644],"domain_scores_gemma":[0.9977315,0.001315631,0.0000744009,0.0003770119,0.0001603028,0.0003411485],"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":[0.0008914294,0.01286563,0.003452462,0.0003967997,0.0005724651,0.002226641,0.0001603617,0.06244673,0.03175974,0.001191533,0.01201796,0.8720183],"study_design_scores_gemma":[0.01249547,0.0008520469,0.02685872,0.002647795,0.001481071,0.002944799,0.0003075536,0.8863848,0.06084166,0.002131317,0.001897029,0.00115771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01741078,0.00005743917,0.9758496,0.004843937,0.0005143412,0.0003735387,0.0002954777,0.0001599905,0.0004948619],"genre_scores_gemma":[0.9338306,0.00000759353,0.06405873,0.001211972,0.0002198136,0.0001056206,0.000313186,0.00004223416,0.0002102737],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9164198,"threshold_uncertainty_score":0.9999962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.014956200096966,"score_gpt":0.3083859375485071,"score_spread":0.2934297374515411,"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."}}