{"id":"W2122643004","doi":"10.1007/s11263-009-0249-6","title":"A Statistical Overlap Prior for Variational Image Segmentation","year":2009,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"London Health Sciences Centre","funders":"","keywords":"Bhattacharyya distance; Segmentation; Image segmentation; Artificial intelligence; Pattern recognition (psychology); Scale-space segmentation; Nonparametric statistics; Mathematics; Gaussian; Computer science; Algorithm; Image (mathematics); Segmentation-based object categorization; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006062577,0.0001191899,0.0001729292,0.0002263307,0.00005429576,0.0003699712,0.0009490044,0.00004836799,0.00004864766],"category_scores_gemma":[0.0001351032,0.0001039887,0.0001162149,0.00008447949,0.00003021347,0.001435045,0.00008591926,0.0001351402,0.00001101516],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001393354,"about_ca_system_score_gemma":0.0001276088,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001482781,"about_ca_topic_score_gemma":1.31243e-7,"domain_scores_codex":[0.9978848,0.00007449521,0.000664826,0.0001959241,0.001037293,0.0001426758],"domain_scores_gemma":[0.9978158,0.0003428,0.0004764258,0.000134447,0.001106998,0.0001235362],"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":[0.0001413034,0.000339526,0.00003561367,0.000006167059,0.00006338184,0.00008069289,0.0002740284,0.0001121245,0.02035019,0.03203042,0.02608394,0.9204826],"study_design_scores_gemma":[0.01053483,0.007711182,0.05324774,0.0004912388,0.00006928907,0.001435797,0.00003535771,0.6449319,0.07819849,0.1911429,0.01133147,0.0008697798],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008334357,0.00002311159,0.9935091,0.003970056,0.001343234,0.0001803136,0.00001156841,0.00005380299,0.00007540234],"genre_scores_gemma":[0.04321399,0.00001937858,0.9537758,0.002240575,0.0006967141,0.000003400739,0.00002230332,0.00000585471,0.00002192618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9196128,"threshold_uncertainty_score":0.4240537,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01000484510054001,"score_gpt":0.3507396387923417,"score_spread":0.3407347936918017,"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."}}