{"id":"W1968562248","doi":"10.1118/1.3590359","title":"An innovative iterative thresholding algorithm for tumour segmentation and volumetric quantification on SPECT images: Monte Carlo-based methodology and validation","year":2011,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Imaging phantom; Thresholding; Segmentation; Monte Carlo method; Computer science; Iterative reconstruction; Single-photon emission computed tomography; Algorithm; Volume (thermodynamics); Image segmentation; Artificial intelligence; Partial volume; Computer vision; Spect imaging; Medical imaging; Calibration; Nuclear medicine; Physics; Mathematics; Optics; Image (mathematics); Statistics; 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":[0.001788772,0.0006514039,0.0005981521,0.0008996179,0.000345099,0.0007283721,0.001220047,0.001313675,0.001227944],"category_scores_gemma":[0.003915464,0.0003432801,0.0007382664,0.0006755817,0.0005720516,0.0005071087,0.0006682886,0.0007026675,0.0004213455],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006914331,"about_ca_system_score_gemma":0.001092214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002832336,"about_ca_topic_score_gemma":0.001812941,"domain_scores_codex":[0.9992618,0.0002331472,0.00004358138,0.00009914891,0.0003210811,0.00004130793],"domain_scores_gemma":[0.9985383,0.0006645972,0.000130018,0.0001393699,0.0004927432,0.00003511636],"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":[0.0002029407,0.0001093764,0.002746089,0.0003919199,0.0001619957,0.0002197861,0.00036868,0.6663488,0.07208332,0.01052679,0.00102067,0.2458195],"study_design_scores_gemma":[0.00001108715,0.00005442028,0.0004661949,0.00002053842,0.00001546818,0.0001000904,0.00001053923,0.9862403,0.01135111,0.0007335069,0.0009796931,0.00001709401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0100933,0.0001000222,0.9886078,0.00003198139,0.00001112387,0.00006158199,0.00002131934,0.0005019596,0.0005708783],"genre_scores_gemma":[0.1448348,0.0001618517,0.8536922,0.00004045992,0.00001037214,0.0002500176,0.0001005074,0.0002655899,0.0006441383],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002832336,"threshold_uncertainty_score":0.009460032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1516123952544418,"score_gpt":0.4043917052350912,"score_spread":0.2527793099806493,"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."}}