{"id":"W2912963483","doi":"10.1002/mp.13436","title":"Convolutional neural network‐based approach for segmentation of left ventricle myocardial scar from 3D late gadolinium enhancement <scp>MR</scp> images","year":2019,"lang":"en","type":"article","venue":"Medical Physics","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Libin Cardiovascular Institute of Alberta; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Convolutional neural network; Segmentation; Ventricle; Sørensen–Dice coefficient; Artificial intelligence; Magnetic resonance imaging; Context (archaeology); Computer science; Medicine; Myocardial infarction; Pattern recognition (psychology); Jaccard index; Image segmentation; Radiology; Cardiology","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":[],"consensus_categories":[],"category_scores_codex":[0.000318637,0.0001737943,0.0004668579,0.00002853732,0.00005253174,0.0000144107,0.00009328486,0.0001042189,0.00005971462],"category_scores_gemma":[0.0003864893,0.0001620749,0.0002667588,0.0001250963,0.0001489181,0.00006539343,0.0000483055,0.0002236892,0.00003218598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006837773,"about_ca_system_score_gemma":0.0002560287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003488559,"about_ca_topic_score_gemma":1.92129e-7,"domain_scores_codex":[0.9980862,0.00005954809,0.0003449934,0.0003113,0.0008394014,0.0003585137],"domain_scores_gemma":[0.9982725,0.0009432453,0.0001465752,0.0002478402,0.0001805984,0.0002092194],"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":[0.0008040901,0.003683221,0.6833823,0.001364447,0.001374004,0.00003999738,0.0008894735,0.08273903,0.02281554,0.0002433752,0.1646724,0.03799215],"study_design_scores_gemma":[0.02755836,0.001285123,0.1248031,0.0006195144,0.001789232,0.00001291993,0.0002361225,0.6635551,0.1732222,0.001492768,0.004948305,0.0004772778],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.52952,0.0007670712,0.4651418,0.0003295379,0.001462066,0.0009982322,0.0002272053,0.00006954309,0.001484602],"genre_scores_gemma":[0.9882625,0.0000255951,0.006467022,0.001304733,0.002161972,0.00003860234,0.001550579,0.00003026591,0.0001587746],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5808161,"threshold_uncertainty_score":0.6609224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0134380586513669,"score_gpt":0.2674383727203933,"score_spread":0.2540003140690265,"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."}}