{"id":"W2013424943","doi":"10.1007/s10278-014-9701-4","title":"Multi-Resolution Level Sets with Shape Priors: A Validation Report for 2D Segmentation of Prostate Gland in T2W MR Images","year":2014,"lang":"en","type":"article","venue":"Journal of Digital Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; King Abdulaziz City for Science and Technology","keywords":"Segmentation; Computer science; Prior probability; Level set (data structures); Context (archaeology); Convergence (economics); Artificial intelligence; Image segmentation; Level set method; Set (abstract data type); Pattern recognition (psychology); Boundary (topology); Medical imaging; Computer vision; Scale-space segmentation; Resolution (logic); Algorithm; Mathematics; Bayesian probability","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006106258,0.001411649,0.001247652,0.002242034,0.0006930348,0.00258647,0.002104204,0.002619243,0.001541503],"category_scores_gemma":[0.01189523,0.001044824,0.001876258,0.0012683,0.0008096074,0.001112194,0.001615264,0.001328399,0.0009675089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006785335,"about_ca_system_score_gemma":0.00136764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01036316,"about_ca_topic_score_gemma":0.008450688,"domain_scores_codex":[0.9977931,0.0007681271,0.000204233,0.0004369575,0.0006559727,0.0001415677],"domain_scores_gemma":[0.9948422,0.003000449,0.0003081863,0.0008912066,0.0008042758,0.0001535432],"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":[0.005431981,0.001773495,0.03235672,0.002873103,0.00239926,0.0008947444,0.001210493,0.351995,0.1147768,0.001596284,0.004966517,0.4797256],"study_design_scores_gemma":[0.0002387202,0.001157299,0.02565708,0.0002029886,0.0005838178,0.001439927,0.0003012781,0.8973585,0.06852449,0.001065828,0.003331159,0.0001388609],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6411526,0.004034,0.338824,0.0004266846,0.0001379411,0.000991868,0.004249678,0.007524409,0.002658736],"genre_scores_gemma":[0.7382885,0.001389724,0.2484501,0.0001948493,0.0000416161,0.0002530825,0.007945369,0.001593627,0.001843011],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01036316,"threshold_uncertainty_score":0.03229338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02949921129377955,"score_gpt":0.3152566962258015,"score_spread":0.285757484932022,"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."}}