{"id":"W2763147268","doi":"10.1109/tbme.2017.2759730","title":"Automatic Temporal Segmentation of Vessels of the Brain Using 4D ASL MRA Images","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Hotchkiss Brain Institute","keywords":"Segmentation; Artificial intelligence; Scale-space segmentation; Computer vision; Computer science; Image segmentation; Pattern recognition (psychology); Frame (networking)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009209254,0.0005561244,0.0002906074,0.002803151,0.0003609195,0.0009596933,0.0004200031,0.0008195317,0.001660078],"category_scores_gemma":[0.00167549,0.0003838307,0.0004563388,0.0008833812,0.0004050906,0.001020379,0.0004006124,0.0003791114,0.0006395046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003447758,"about_ca_system_score_gemma":0.0004684536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008988981,"about_ca_topic_score_gemma":0.001885645,"domain_scores_codex":[0.9997025,0.00007961829,0.00001942271,0.00006511207,0.0001031389,0.00003018116],"domain_scores_gemma":[0.999473,0.0001520721,0.0001186182,0.00005787882,0.0001655334,0.00003290534],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005124047,0.00008816448,0.006687404,0.0004911275,0.0001015907,0.0005888935,0.0003026872,0.00886166,0.7689896,0.00142366,0.001920766,0.2100321],"study_design_scores_gemma":[0.00009733949,0.0006238963,0.05511676,0.0001826869,0.000369927,0.006530706,0.0004608508,0.3690872,0.547222,0.004335616,0.01579636,0.00017654],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2608457,0.001711762,0.7314048,0.0003571409,0.00006363147,0.0001778817,0.0005075686,0.002354863,0.002576672],"genre_scores_gemma":[0.4569458,0.001183394,0.5391067,0.0001336087,0.0000855146,0.0002131567,0.0004780918,0.0002881587,0.001565643],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002803151,"threshold_uncertainty_score":0.005553544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01853787358857887,"score_gpt":0.2880497906114136,"score_spread":0.2695119170228347,"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."}}