{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01955198,0.002452798,0.002379493,0.009632655,0.001217691,0.004260099,0.002167536,0.003299424,0.002535128],"category_scores_gemma":[0.07328903,0.0006150341,0.00179314,0.006285174,0.001542896,0.003727668,0.001857795,0.001281639,0.0013398],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002035155,"about_ca_system_score_gemma":0.001843654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002101826,"about_ca_topic_score_gemma":0.001985887,"domain_scores_codex":[0.9743884,0.007621779,0.002371,0.001812175,0.01338041,0.0004263387],"domain_scores_gemma":[0.9529883,0.03008814,0.002940742,0.003421169,0.01010905,0.0004525817],"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":[0.001055783,0.0002457764,0.007662743,0.002206852,0.00111245,0.0001656013,0.0003447838,0.1416074,0.01961643,0.0153503,0.008428165,0.8022036],"study_design_scores_gemma":[0.0001601147,0.001691229,0.01169906,0.0005877761,0.0005783799,0.001493477,0.0004053366,0.8619041,0.07354482,0.02294249,0.02479796,0.0001952015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02854536,0.009770855,0.9509585,0.0005832086,0.0003837014,0.0007982417,0.0007395467,0.003363371,0.004857143],"genre_scores_gemma":[0.161177,0.004843269,0.8280877,0.0002351284,0.0001934549,0.0005828794,0.002129341,0.001048328,0.001702927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01955198,"threshold_uncertainty_score":0.103402,"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."}}