{"id":"W3162717073","doi":"10.18280/ts.380207","title":"A Framework for Multi-Threshold Image Segmentation of Low Contrast Medical Images","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Contouring; Artificial intelligence; Image segmentation; Fuzzy logic; Segmentation; Pattern recognition (psychology); Entropy (arrow of time); Computer science; Membership function; Segmentation-based object categorization; Scale-space segmentation; Region growing; Mathematics; Computer vision; Fuzzy set","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001017988,0.0006562675,0.000941926,0.001545118,0.0006456674,0.001187311,0.001623006,0.001276859,0.001781723],"category_scores_gemma":[0.001436343,0.0004424021,0.001358058,0.001172038,0.0008142375,0.001090523,0.001264751,0.001140629,0.0007262934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008997426,"about_ca_system_score_gemma":0.001101158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002902524,"about_ca_topic_score_gemma":0.00268023,"domain_scores_codex":[0.9992951,0.0001255313,0.0000451144,0.0001386222,0.0003428313,0.00005277789],"domain_scores_gemma":[0.9996439,0.0001066235,0.00004845084,0.00005027215,0.0001158859,0.00003482296],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001479558,0.00009198285,0.0007941379,0.0004821154,0.000162632,0.0006365177,0.0004061281,0.3450733,0.1140338,0.1993002,0.004978995,0.3338921],"study_design_scores_gemma":[0.000009054329,0.00006412836,0.000367899,0.00003070305,0.00002788252,0.0004870774,0.00002858473,0.9552966,0.00900927,0.02511146,0.009532839,0.00003441328],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004845927,0.0001414415,0.9989416,0.00002322696,0.000009456267,0.00001449294,0.000008823309,0.0001123489,0.0002639884],"genre_scores_gemma":[0.05804677,0.0005454667,0.939393,0.0000566013,0.00006586801,0.0001059074,0.00009465034,0.0001258436,0.00156585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002902524,"threshold_uncertainty_score":0.006528139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03228202158045575,"score_gpt":0.3353799145774809,"score_spread":0.3030978929970252,"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."}}