{"id":"W2140584173","doi":"10.1109/tbme.2010.2048752","title":"Tracking Endocardial Motion Via Multiple Model Filtering","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; St Joseph's Health Care; CARE Canada; London Health Sciences Centre; General Electric (Canada)","funders":"","keywords":"Segmentation; Tracking (education); Artificial intelligence; Computer science; Computer vision; Motion estimation; Motion (physics); Pattern recognition (psychology); Mathematics","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":[0.001065513,0.000519319,0.0008000275,0.001051814,0.0003091177,0.0006600781,0.000715377,0.001033714,0.0007471951],"category_scores_gemma":[0.002233067,0.0004349571,0.0009269472,0.0008225486,0.000329757,0.0008209665,0.0006761083,0.0007929463,0.0003444118],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005608658,"about_ca_system_score_gemma":0.0006693852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004512015,"about_ca_topic_score_gemma":0.004909846,"domain_scores_codex":[0.9994924,0.0001103831,0.00002878211,0.0001316524,0.0001903952,0.00004632043],"domain_scores_gemma":[0.999465,0.0002884704,0.0000742252,0.00007415374,0.00007933109,0.00001870596],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002211366,0.0001228226,0.002485273,0.0001110487,0.0001449685,0.0001234752,0.0001508154,0.4866273,0.059394,0.009035149,0.001271951,0.4403121],"study_design_scores_gemma":[0.00000548795,0.00002369751,0.0006336774,0.000003371362,0.00001061916,0.00003758568,0.00000475029,0.9926072,0.004327895,0.001760752,0.0005761876,0.000008767804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009420206,0.00007335428,0.9898973,0.00003631892,0.00001343821,0.00001037614,0.00001737425,0.0002983989,0.0002332842],"genre_scores_gemma":[0.3886651,0.0003996742,0.6077054,0.00008818615,0.00005020911,0.00009461588,0.0002992135,0.0001168197,0.002580794],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004512015,"threshold_uncertainty_score":0.008971512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01228075222450115,"score_gpt":0.2390316264472764,"score_spread":0.2267508742227752,"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."}}