{"id":"W2944775438","doi":"10.1002/mp.13581","title":"Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images","year":2019,"lang":"en","type":"article","venue":"Medical Physics","topic":"Cardiovascular Health and Disease Prevention","field":"Medicine","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thrombosis and Atherosclerosis Research Institute; Robarts Clinical Trials; Western University","funders":"National Natural Science Foundation of China","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Pixel; Image segmentation; Convolutional neural network; Pattern recognition (psychology); Ultrasound; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008293023,0.0009173642,0.0006987506,0.001260363,0.0003262675,0.0007410273,0.0008658193,0.0008302447,0.001051294],"category_scores_gemma":[0.001496049,0.0004540252,0.0008139113,0.0006030808,0.0003258574,0.0005700635,0.0006882964,0.0007519352,0.000675594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006845496,"about_ca_system_score_gemma":0.001024746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008283306,"about_ca_topic_score_gemma":0.0100923,"domain_scores_codex":[0.9997002,0.00004125895,0.00001890713,0.00009853656,0.00008404504,0.00005704001],"domain_scores_gemma":[0.9996251,0.0001018853,0.00005728867,0.00004928805,0.0001382249,0.00002816427],"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":[0.0006555432,0.000456213,0.009465904,0.000230492,0.0001841959,0.0002547948,0.0001682858,0.1940555,0.1250057,0.001211645,0.002764134,0.6655477],"study_design_scores_gemma":[0.00001125187,0.0001052505,0.004045208,0.00001994031,0.00003716645,0.0001137412,0.00002124903,0.9607536,0.03366316,0.0006260122,0.0005866416,0.000016747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3450107,0.001115502,0.6449232,0.0002217531,0.00006769811,0.0002204702,0.0006817766,0.0050106,0.00274841],"genre_scores_gemma":[0.7532806,0.0004878675,0.2397798,0.0001771664,0.00003015492,0.0002341246,0.002075617,0.0001959388,0.003738719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008283306,"threshold_uncertainty_score":0.01647019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007282377901163208,"score_gpt":0.2584508788477111,"score_spread":0.2511685009465479,"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."}}