{"id":"W2963924907","doi":"10.1007/978-3-030-04375-9_31","title":"IVUS-Net: An Intravascular Ultrasound Segmentation Network","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Coronary Interventions and Diagnostics","field":"Medicine","cited_by":59,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Intravascular ultrasound; Computer science; Jaccard index; Artificial intelligence; Segmentation; Hausdorff distance; Test set; Computer vision; Lumen (anatomy); Adventitia; Zoom; Pattern recognition (psychology); Radiology; Medicine; Optics; Pathology","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":[0.0004504237,0.0008086686,0.0005336448,0.001214184,0.0003126614,0.0009489155,0.001293925,0.000655077,0.01011217],"category_scores_gemma":[0.001100604,0.0005697566,0.0005002503,0.0008020251,0.0002185169,0.001024109,0.00116287,0.0007367999,0.00554023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005825612,"about_ca_system_score_gemma":0.0006350157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003268332,"about_ca_topic_score_gemma":0.004964299,"domain_scores_codex":[0.9998437,0.00001985271,0.000009947963,0.00004547836,0.00006575871,0.00001520275],"domain_scores_gemma":[0.9997724,0.00007637586,0.00001668707,0.00004771291,0.00005995084,0.00002693834],"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":[0.0004611141,0.00009934088,0.001148717,0.0001733497,0.00009409335,0.0001408168,0.00005556031,0.03610609,0.01231876,0.007287155,0.0924842,0.8496307],"study_design_scores_gemma":[0.00006605052,0.0001153286,0.001558271,0.00008493139,0.00008390614,0.0004541645,0.00003613097,0.8345845,0.02544926,0.02307303,0.1144294,0.00006504945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01022676,0.001346508,0.922815,0.0004422319,0.0003343153,0.000232118,0.003977822,0.04857907,0.01204623],"genre_scores_gemma":[0.08861992,0.001663916,0.8542146,0.0006731189,0.0002660826,0.0004379716,0.01303887,0.004388358,0.03669712],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01011217,"threshold_uncertainty_score":0.03382856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01620276926096933,"score_gpt":0.2761491904665572,"score_spread":0.2599464212055879,"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."}}