{"id":"W2926054458","doi":"10.1016/j.ultras.2019.03.014","title":"Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net","year":2019,"lang":"en","type":"article","venue":"Ultrasonics","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":125,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Jaccard index; Intravascular ultrasound; Computer science; Artificial intelligence; Hausdorff distance; Segmentation; Computer vision; Lumen (anatomy); Pattern recognition (psychology); Path (computing); 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":[0.0008011204,0.001161422,0.0007398849,0.001199848,0.0003172676,0.000802459,0.001320403,0.001350221,0.001033707],"category_scores_gemma":[0.001401703,0.0005457706,0.0006533138,0.0006058262,0.0004914395,0.0007624046,0.00107272,0.0007186568,0.0006299426],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008641112,"about_ca_system_score_gemma":0.001157693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009317646,"about_ca_topic_score_gemma":0.01194132,"domain_scores_codex":[0.9996735,0.00005654099,0.00001773662,0.0001101748,0.00007811828,0.00006388177],"domain_scores_gemma":[0.9996459,0.0001181297,0.00005508651,0.00005271361,0.00009464465,0.00003351313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001136138,0.0002624568,0.005044964,0.0002169509,0.0002013562,0.000476684,0.0001450419,0.2564662,0.07556825,0.002548243,0.008829048,0.6491047],"study_design_scores_gemma":[0.00001116502,0.00007679263,0.0009334738,0.00001589256,0.0000230461,0.0001191692,0.00001329754,0.9830523,0.01397966,0.0008001251,0.0009631858,0.00001190966],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1669709,0.001914198,0.8177168,0.0004607452,0.0001413219,0.0001526547,0.0006739392,0.00860961,0.003359678],"genre_scores_gemma":[0.5742951,0.000798754,0.416696,0.000547045,0.00006465561,0.0001185358,0.002271526,0.0002785458,0.004929783],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009317646,"threshold_uncertainty_score":0.01852679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009179317351332747,"score_gpt":0.2022009185234941,"score_spread":0.1930216011721614,"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."}}