{"id":"W2091928694","doi":"10.1016/j.acra.2015.03.010","title":"Validation of a Semiautomated Liver Segmentation Method Using CT for Accurate Volumetry","year":2015,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Hepatocellular Carcinoma Treatment and Prognosis","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal; McGill University; Hôpital Saint-Luc; Montreal General Hospital","funders":"","keywords":"Repeatability; Medicine; Segmentation; Intraclass correlation; Nuclear medicine; Hepatocellular carcinoma; Radiology; Artificial intelligence; Mathematics; Computer science; Internal medicine","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":[0.005695391,0.0008434757,0.0007801407,0.002312466,0.0006851903,0.002275191,0.001504999,0.001773736,0.002180736],"category_scores_gemma":[0.01460118,0.0008230135,0.0009114041,0.001001394,0.0006528582,0.0008009201,0.0008904401,0.0006114312,0.001132728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007391925,"about_ca_system_score_gemma":0.001549476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004622069,"about_ca_topic_score_gemma":0.004552307,"domain_scores_codex":[0.9971951,0.0009801161,0.0003614835,0.0004162638,0.0009450526,0.0001018885],"domain_scores_gemma":[0.9893175,0.004481025,0.0006358946,0.001643811,0.003766707,0.0001550697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002572407,0.0005441983,0.05725728,0.0008511735,0.0006465449,0.0004137635,0.000660338,0.06321773,0.3931171,0.001963463,0.003537333,0.4752188],"study_design_scores_gemma":[0.0003119526,0.001125531,0.06916823,0.0001208914,0.0005631493,0.002937918,0.0002128748,0.6687497,0.2482817,0.001331657,0.006957243,0.0002391217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2775673,0.0009275565,0.7128239,0.0002823311,0.000267527,0.0006674081,0.0008540404,0.004588838,0.002021168],"genre_scores_gemma":[0.5434964,0.0002092748,0.4530446,0.0001531376,0.00004460368,0.0002805327,0.001094843,0.0007185213,0.0009581046],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005695391,"threshold_uncertainty_score":0.03012043,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.20814046061971,"score_gpt":0.3856816091583677,"score_spread":0.1775411485386577,"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."}}