{"id":"W2954299637","doi":"10.1109/tmi.2020.3003240","title":"Cardiac Segmentation With Strong Anatomical Guarantees","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Cardiovascular Function and Risk Factors","field":"Medicine","cited_by":119,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Segmentation; Convolutional neural network; Image warping; Autoencoder; Image segmentation; Pattern recognition (psychology); Representation (politics); Cardiac imaging; Medical imaging","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":[0.001540302,0.0009687034,0.0007575232,0.0006865963,0.0004134869,0.001439432,0.001203213,0.001800381,0.001558699],"category_scores_gemma":[0.008125958,0.00106771,0.0008873589,0.0003906091,0.001338978,0.001634591,0.002840116,0.002216376,0.0008078211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009253594,"about_ca_system_score_gemma":0.001719868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003712241,"about_ca_topic_score_gemma":0.006671143,"domain_scores_codex":[0.9990858,0.0001648406,0.0000555548,0.0002615654,0.0003490732,0.0000832507],"domain_scores_gemma":[0.9979411,0.0007361622,0.0002684087,0.0005712346,0.0003707056,0.0001124807],"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":[0.0002311368,0.00005003267,0.001789708,0.0001384773,0.00007523411,0.00017136,0.0001679642,0.8060182,0.03658311,0.02737724,0.002642247,0.1247553],"study_design_scores_gemma":[0.000008881862,0.00002991295,0.0003267707,0.00001338284,0.000006719002,0.00008495862,0.000008134732,0.9833265,0.004878998,0.01044608,0.0008597681,0.000009804725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01983406,0.0002372681,0.9765139,0.0002948833,0.00003040485,0.00002415432,0.0001155823,0.001037491,0.001912224],"genre_scores_gemma":[0.6536195,0.0004875798,0.3385286,0.0006188182,0.0001207299,0.00009234693,0.000701562,0.0008603901,0.004970587],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003712241,"threshold_uncertainty_score":0.008145988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01249647550197962,"score_gpt":0.2618795285493968,"score_spread":0.2493830530474172,"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."}}