{"id":"W2773829802","doi":"10.1016/j.ultras.2017.11.020","title":"Segmentation of arterial walls in intravascular ultrasound cross-sectional images using extremal region selection","year":2017,"lang":"en","type":"article","venue":"Ultrasonics","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Universitat de Barcelona","keywords":"Intravascular ultrasound; Segmentation; Adventitia; Lumen (anatomy); Hausdorff distance; Computer science; Artificial intelligence; Pixel; Gold standard (test); Artifact (error); Computer vision; Biomedical engineering; Pattern recognition (psychology); Materials science; Radiology; Medicine; Anatomy","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":[0.001116934,0.0008303856,0.000767034,0.003467109,0.0005690953,0.001343212,0.0006295645,0.001073302,0.001186972],"category_scores_gemma":[0.001335551,0.000628065,0.0008914906,0.001152333,0.0004054972,0.0004900498,0.0005571874,0.000488062,0.0006018781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002231379,"about_ca_system_score_gemma":0.0009789305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001691051,"about_ca_topic_score_gemma":0.002369794,"domain_scores_codex":[0.9996825,0.0000766456,0.00003103444,0.00007123018,0.0000686378,0.00007001132],"domain_scores_gemma":[0.9994904,0.0002096729,0.00006840024,0.00005098639,0.0001306577,0.00004989153],"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":[0.001497242,0.0002836133,0.01430493,0.0005320921,0.0002052249,0.001551089,0.0005843358,0.02112969,0.423326,0.003180485,0.00259812,0.5308071],"study_design_scores_gemma":[0.0001149058,0.0004528563,0.0500524,0.0001846116,0.0005210058,0.00366213,0.0004501323,0.7047311,0.2289394,0.003083856,0.007695779,0.0001118626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2335739,0.001352375,0.7595785,0.000246011,0.0000432371,0.000308727,0.0004490794,0.002386598,0.002061496],"genre_scores_gemma":[0.3756775,0.0008229191,0.6208371,0.00008680661,0.00006772353,0.0001622007,0.0006911619,0.0003912757,0.001263287],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003467109,"threshold_uncertainty_score":0.00590694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04052785701115246,"score_gpt":0.3433108326998302,"score_spread":0.3027829756886777,"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."}}