{"id":"W2891195353","doi":"10.1007/978-3-030-00889-5_4","title":"A Unified Framework Integrating Recurrent Fully-Convolutional Networks and Optical Flow for Segmentation of the Left Ventricle in Echocardiography Data","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vancouver General Hospital; University of British Columbia","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Optical flow; Ventricle; Speckle pattern; Computer vision; Pattern recognition (psychology); Operator (biology); Medicine; Image (mathematics); Cardiology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003338634,0.0001748734,0.0003105839,0.000185008,0.00007673008,0.00003941766,0.0002720825,0.0001426614,0.000007503062],"category_scores_gemma":[0.000160987,0.0001225997,0.0002768532,0.0002329581,0.0003641022,0.00006786446,0.0003159759,0.0002837973,3.710411e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001016941,"about_ca_system_score_gemma":0.0001668029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006742709,"about_ca_topic_score_gemma":0.00001292645,"domain_scores_codex":[0.9986515,0.00001784569,0.0002562057,0.0005232851,0.0003497064,0.0002014413],"domain_scores_gemma":[0.9988358,0.0003635772,0.0001089906,0.0005242567,0.00009914993,0.00006825233],"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":[0.0004848299,0.0002649754,0.1645697,0.0002561017,0.0005861505,0.00002226433,0.0007249948,0.01755685,0.00003650651,0.003034981,0.000111813,0.8123508],"study_design_scores_gemma":[0.002006388,0.0005780849,0.1295446,0.003484332,0.000565407,0.00002285886,0.000004143918,0.8129306,0.0001053104,0.05029495,0.00008552321,0.0003777215],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009512593,0.001860453,0.9865509,0.0001547256,0.0009479479,0.0008185216,0.00008161115,0.000009587737,0.00006369756],"genre_scores_gemma":[0.8660976,0.0001007203,0.1329475,0.0002469563,0.0004191862,0.000009999058,0.000155128,0.00001628786,0.000006564703],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.856585,"threshold_uncertainty_score":0.4999471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02201473749328299,"score_gpt":0.3186376097909051,"score_spread":0.2966228722976221,"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."}}