{"id":"W2914416139","doi":"10.1016/j.compbiomed.2019.01.023","title":"Automatic IVUS lumen segmentation using a 3D adaptive helix model","year":2019,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Heart Institute; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Initialization; Computer science; Artificial intelligence; Lumen (anatomy); Intravascular ultrasound; Computer vision; Hausdorff distance; Pattern recognition (psychology); Jaccard index; 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":[0.0001423829,0.00009458178,0.0002615394,0.000144924,0.00002669499,0.000001783648,0.00003604727,0.00007509195,0.0001391241],"category_scores_gemma":[0.0000319649,0.00007317935,0.00002381469,0.0000863262,0.0001115968,0.0000327825,0.00004293957,0.0001059605,0.000007867344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005691991,"about_ca_system_score_gemma":0.00003265051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004847863,"about_ca_topic_score_gemma":0.000007023381,"domain_scores_codex":[0.9993784,0.00003503452,0.0002204741,0.0001820721,0.0000490144,0.0001350324],"domain_scores_gemma":[0.9996099,0.0001283239,0.00005470885,0.0001157676,0.00003196942,0.00005929186],"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":[0.0007490112,0.0008466688,0.5769366,0.0009228154,0.0004094529,0.0001741536,0.006000997,0.003598548,0.0396526,0.01370992,0.002780332,0.3542189],"study_design_scores_gemma":[0.003227115,0.001319778,0.02661379,0.0008648774,0.00007143199,0.0001065317,0.0003262254,0.9655408,0.0000418668,0.001726638,0.00007302788,0.00008790955],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9509638,0.0007587207,0.04645486,0.0005524625,0.0004095695,0.0003317195,0.000002810243,0.00002457384,0.0005015132],"genre_scores_gemma":[0.9700093,0.0001192894,0.0287159,0.0009268733,0.00007603817,0.00000656621,0.00003828859,0.00000602304,0.0001017236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9619423,"threshold_uncertainty_score":0.2984167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03602764491480034,"score_gpt":0.3548334379047259,"score_spread":0.3188057929899255,"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."}}