{"id":"W2751806885","doi":"10.1002/mp.12560","title":"Comparison of vessel enhancement algorithms applied to time‐of‐flight MRA images for cerebrovascular segmentation","year":2017,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Segmentation; Algorithm; Computer science; Medical imaging; Computer vision; Image segmentation; Image enhancement; Artificial intelligence; Image (mathematics)","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":[0.0005392701,0.0001365981,0.0003921813,0.00004161483,0.0001313956,0.00005726576,0.001255519,0.00006829006,0.0000718196],"category_scores_gemma":[0.0002244233,0.0001227931,0.00009550052,0.0001073214,0.0001940282,0.000249626,0.0003198572,0.0001021281,0.00002851516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003432513,"about_ca_system_score_gemma":0.00008989338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001496792,"about_ca_topic_score_gemma":2.618202e-7,"domain_scores_codex":[0.9978705,0.00003088249,0.000482823,0.0003226286,0.001081458,0.0002117088],"domain_scores_gemma":[0.9983573,0.0001348669,0.0003844932,0.0007497297,0.0001721864,0.0002014116],"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":[0.00001209122,0.0005575041,0.0001632574,0.000187278,0.00007058296,7.742354e-7,0.00109978,0.000007532054,0.1134207,0.001232002,0.01828834,0.8649601],"study_design_scores_gemma":[0.0005771596,0.0001731681,0.000269572,0.00007363755,0.00001853117,1.998231e-7,0.00002220563,0.0046994,0.991738,0.002039043,0.0002656312,0.0001234531],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001540059,0.00001947685,0.996375,0.000674494,0.0001741754,0.0006169362,0.000007285468,0.00005880218,0.0005337678],"genre_scores_gemma":[0.4185522,0.00001918872,0.5802123,0.0005573629,0.0002209494,0.0002392135,0.00004064851,0.00001924566,0.000138851],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8783173,"threshold_uncertainty_score":0.5007359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02724766774544512,"score_gpt":0.3581398000006968,"score_spread":0.3308921322552517,"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."}}