{"id":"W3091088513","doi":"10.1016/j.media.2020.101916","title":"Fully automated left atrium segmentation from anatomical cine long-axis MRI sequences using deep convolutional neural network with unscented Kalman filter","year":2020,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; Canadian VIGOUR Centre","funders":"Servier; Mitacs","keywords":"Artificial intelligence; Convolutional neural network; Segmentation; Computer science; Kalman filter; Sørensen–Dice coefficient; Pattern recognition (psychology); Computer vision; Artificial neural network; Image segmentation; Deep learning; Ground truth","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001376586,0.0002388025,0.0005756904,0.0001204235,0.000158385,0.00003351413,0.0001786089,0.0001442061,0.002735921],"category_scores_gemma":[0.0001044114,0.000184723,0.0002267459,0.001431426,0.0003477817,0.0001730238,0.00007287519,0.0003581601,0.00002082757],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001126733,"about_ca_system_score_gemma":0.0001260553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000264986,"about_ca_topic_score_gemma":0.0001794858,"domain_scores_codex":[0.9977027,0.00008473476,0.0005126565,0.0005511758,0.0007893267,0.0003594357],"domain_scores_gemma":[0.9986587,0.0001003869,0.0001843084,0.0002736077,0.0002247962,0.0005581633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001973636,0.001102045,0.7821749,0.0001698082,0.006660121,0.002768704,0.0008957062,0.1083173,0.06876878,0.000245136,0.01977495,0.007148904],"study_design_scores_gemma":[0.001018184,0.0001314665,0.03519022,0.00004631796,0.002348362,0.00003845103,0.0001023529,0.9593573,0.001302377,0.00004802482,0.0002126786,0.0002042883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2565718,0.000196287,0.7306461,0.01171765,0.0000186371,0.0002936371,0.00003828614,0.0004778573,0.00003971456],"genre_scores_gemma":[0.8745783,0.00006188433,0.1192659,0.00364597,0.0004355444,0.00001968698,0.001940524,0.00002653848,0.00002570234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.85104,"threshold_uncertainty_score":0.9981757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682712962209134,"score_gpt":0.3152889253414733,"score_spread":0.298461795719382,"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."}}