{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007888144,0.0001405636,0.0002561961,0.000124825,0.0001130309,0.00002252316,0.00006862027,0.00008132408,0.00002588915],"category_scores_gemma":[0.0005002046,0.0001178637,0.00002909929,0.0004365745,0.000223047,0.0001295582,0.00001791397,0.0002263071,0.000001675911],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004249755,"about_ca_system_score_gemma":0.00007422287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006676612,"about_ca_topic_score_gemma":7.736403e-7,"domain_scores_codex":[0.9987983,0.0001054752,0.0002712698,0.0003642875,0.0003016218,0.0001591069],"domain_scores_gemma":[0.9989082,0.0003478515,0.0001361619,0.0001854155,0.000237117,0.0001852658],"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":[0.0001674271,0.0009103528,0.003838702,0.0001624564,0.00007630482,0.00001250687,0.001928405,0.000002525244,0.03884775,0.005220715,0.00173509,0.9470978],"study_design_scores_gemma":[0.00250886,0.001695254,0.01146586,0.0002219834,0.0001798721,0.00001937017,0.0003361299,0.1994337,0.7755851,0.008014398,0.0002305355,0.0003090417],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1398088,0.00002826791,0.8580268,0.001195969,0.00004441527,0.0006964548,0.00002574627,0.00008699706,0.00008654509],"genre_scores_gemma":[0.744556,0.00002689624,0.2540005,0.0008743402,0.0001652363,0.0001916825,0.0001502598,0.00001851552,0.00001657081],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9467887,"threshold_uncertainty_score":0.4806342,"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."}}