{"id":"W2040101654","doi":"10.1118/1.3476110","title":"Poster — Thur Eve — 05: Semi‐Automated Segmentation of Lung Tumours on CT Scans Using Level Set Sparse Field Active Model","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"","keywords":"Thresholding; Segmentation; Active contour model; Artificial intelligence; Image segmentation; Pattern recognition (psychology); Computer science; Standard deviation; Level set (data structures); Nuclear medicine; Computer vision; Mathematics; Medicine; Image (mathematics); Statistics","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":[0.001104353,0.0006954753,0.0005306939,0.001272862,0.0003188331,0.001349668,0.0008903244,0.001370606,0.003889844],"category_scores_gemma":[0.001535911,0.0004401728,0.0009889025,0.0005646475,0.0004327458,0.0008628219,0.0006884214,0.0007907192,0.002540593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003872516,"about_ca_system_score_gemma":0.0007498923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001613332,"about_ca_topic_score_gemma":0.002788804,"domain_scores_codex":[0.9994098,0.0001406106,0.00003736573,0.0001006002,0.0002684596,0.00004321152],"domain_scores_gemma":[0.9993191,0.0001822358,0.00007206622,0.0001370475,0.0002419497,0.00004762482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003856787,0.0002144162,0.002316987,0.0002931175,0.0001722219,0.0003574215,0.0001820355,0.1418447,0.2001488,0.003623351,0.01128815,0.639173],"study_design_scores_gemma":[0.00002570693,0.0001569491,0.002521236,0.00002331651,0.00002863819,0.0005533468,0.00002882677,0.9095873,0.07624792,0.001744958,0.009032384,0.00004947363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03351531,0.0003986153,0.9593869,0.0002898841,0.0001269768,0.0002047533,0.0002725485,0.002943144,0.002861891],"genre_scores_gemma":[0.2126108,0.0004220546,0.7727732,0.0001854681,0.0001380115,0.0001585484,0.001400154,0.0005431413,0.01176855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003889844,"threshold_uncertainty_score":0.01301289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03206057481821158,"score_gpt":0.3480162252543809,"score_spread":0.3159556504361693,"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."}}