{"id":"W2163482106","doi":"10.1109/tns.2005.858208","title":"A robust visual tracking system for patient motion detection in SPECT: hardware solutions","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Nuclear Science","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering","keywords":"Computer vision; Imaging phantom; Computer science; Tracking (education); Artificial intelligence; Calibration; Tracking system; Match moving; Synchronization (alternating current); Visualization; Motion (physics); Computer graphics (images); Filter (signal processing); Physics; Optics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003390818,0.000102543,0.0001335601,0.000322075,0.0005279883,0.00004965213,0.0001097663,0.00005144867,0.00003787849],"category_scores_gemma":[0.00002094831,0.00009846801,0.00008255931,0.0007142319,0.0001901378,0.000254083,0.000001949014,0.0002354594,0.00004164325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005345078,"about_ca_system_score_gemma":0.00006754087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004177729,"about_ca_topic_score_gemma":0.00004410545,"domain_scores_codex":[0.9987456,0.00001537919,0.0002536723,0.0003539241,0.0003259598,0.000305444],"domain_scores_gemma":[0.9994265,0.00003026758,0.00005447063,0.0002212539,0.0001139001,0.0001536272],"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":[0.000142208,0.001409547,0.00001524474,0.0001144648,0.00001247559,0.000006527733,0.0009805192,0.0257928,0.1978735,0.0005489858,0.0002562492,0.7728475],"study_design_scores_gemma":[0.0005974378,0.0003626578,0.0004082439,0.0002428089,0.00003938898,0.000106742,0.0004861057,0.8979691,0.09739686,0.000009489415,0.002230338,0.0001507999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3177121,0.000004754244,0.6789802,0.001880148,0.0001830048,0.0006999859,0.00001027433,0.0003284426,0.000201147],"genre_scores_gemma":[0.9833077,0.000007405372,0.01625951,0.0001877883,0.00006037094,0.0001220557,4.583386e-7,0.00001878277,0.00003598266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8721763,"threshold_uncertainty_score":0.4060912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03861122345115406,"score_gpt":0.2927527585623705,"score_spread":0.2541415351112164,"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."}}