{"id":"W2054516549","doi":"10.1155/2013/980769","title":"Automatic Segmentation of Lung Carcinoma Using 3D Texture Features in 18-FDG PET/CT","year":2013,"lang":"en","type":"article","venue":"International Journal of Molecular Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Sunnybrook Health Science Centre; Health Sciences Centre; McGill University","funders":"","keywords":"Artificial intelligence; Voxel; Segmentation; Ground truth; Positron emission tomography; Thresholding; Pattern recognition (psychology); Medicine; Hounsfield scale; Sørensen–Dice coefficient; Nuclear medicine; Computer science; Image segmentation; Radiology; Computed tomography; Image (mathematics)","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":[0.0003357817,0.0001194277,0.0002639894,0.000473829,0.00001953435,0.00005877111,0.0002059716,0.000009210592,0.0002322867],"category_scores_gemma":[0.0003322359,0.0001028747,0.0001353187,0.0001220082,0.00005863516,0.0002432972,0.0000467477,0.0004379761,0.000001930464],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001989911,"about_ca_system_score_gemma":0.0001304499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002426719,"about_ca_topic_score_gemma":7.231421e-7,"domain_scores_codex":[0.9984589,0.00007080963,0.0005479042,0.00011566,0.0006549445,0.0001517876],"domain_scores_gemma":[0.9989395,0.00005551366,0.000422759,0.00009526478,0.0003951324,0.00009188778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005694752,0.0002010102,0.3699768,0.0001516566,0.0003253427,0.009326479,0.0005000612,0.003324505,0.5169245,0.00008436907,0.001528084,0.09760027],"study_design_scores_gemma":[0.004399228,0.00007012236,0.2128934,0.001778808,0.0002086839,0.02889114,0.0005454542,0.7226549,0.02765918,0.0005033787,0.0001599681,0.0002357075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822947,0.0009138942,0.01260456,0.003158747,0.0004071232,0.0001358215,0.000001052786,0.000007929087,0.0004762161],"genre_scores_gemma":[0.9740797,0.00001880159,0.02479006,0.0009330309,0.000128473,0.000001692862,0.000006481279,0.00002115384,0.00002061669],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7193304,"threshold_uncertainty_score":0.4195109,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005692390877067885,"score_gpt":0.305973318859074,"score_spread":0.3002809279820061,"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."}}