{"id":"W2158914083","doi":"10.1109/iembs.2005.1616166","title":"Evaluation of Segmentation algorithms for Medical Imaging","year":2005,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":137,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer science; Weighting; Image segmentation; Artificial intelligence; Market segmentation; Task (project management); Process (computing); Metric (unit); Matching (statistics); Scale-space segmentation; Medical imaging; Segmentation-based object categorization; Machine learning; Object (grammar); Algorithm; Computer vision; Pattern recognition (psychology); Data mining; Mathematics; 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":[0.003278926,0.00005282014,0.00007276962,0.00007786841,0.00002905414,0.00002682708,0.0003157858,0.00002612558,0.0004577225],"category_scores_gemma":[0.0004438481,0.00004608455,0.00003307713,0.0001270321,0.00003429944,0.0005355946,0.00005287271,0.00003474994,0.000009217342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007291448,"about_ca_system_score_gemma":0.000162306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009534873,"about_ca_topic_score_gemma":0.00000432898,"domain_scores_codex":[0.9978863,0.0000886054,0.0002730971,0.0001676177,0.001479889,0.0001045009],"domain_scores_gemma":[0.9991014,0.000105023,0.00008309555,0.0001709696,0.0004661512,0.00007338227],"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":[5.126761e-7,0.00003287032,0.00001913667,0.000004145566,0.000004519307,1.065398e-7,0.000128581,0.00001004659,0.002943496,0.001316271,0.003126725,0.9924136],"study_design_scores_gemma":[0.0004955538,0.00001482482,0.00007539031,0.000008868391,0.00001090381,0.000002677502,0.00003173859,0.7302472,0.2669884,0.001927477,0.0001527855,0.00004417117],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002760698,0.00006551277,0.9949957,0.002539657,0.00009273034,0.0003742169,7.746626e-7,0.0001364677,0.001518897],"genre_scores_gemma":[0.05439662,0.000008324005,0.944239,0.001090589,0.00008091243,0.0001089706,0.000007386711,0.000004075715,0.00006408638],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9923694,"threshold_uncertainty_score":0.5011741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04227467396662234,"score_gpt":0.3925639867987006,"score_spread":0.3502893128320783,"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."}}