{"id":"W2975428498","doi":"10.18280/ts.360310","title":"An Optic Disc Segmentation Method Based on Active Contour Tracking","year":2019,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province","keywords":"Optic disc; Artificial intelligence; Segmentation; Computer science; Computer vision; Active contour model; Contrast (vision); Retinal; Robustness (evolution); Fundus (uterus); Optic cup (embryology); Image segmentation; Pattern recognition (psychology); Ophthalmology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003522045,0.0001401389,0.0002446796,0.0001328884,0.00005328752,0.00004524268,0.00006206523,0.00003117488,0.001429516],"category_scores_gemma":[0.00001211011,0.0001113665,0.0001234844,0.0001314561,0.0000171397,0.0001483767,0.000004122651,0.0001298685,0.00006878793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008722908,"about_ca_system_score_gemma":0.00003753474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003504952,"about_ca_topic_score_gemma":0.00000146042,"domain_scores_codex":[0.9988309,0.0001252398,0.0002038419,0.0002815805,0.0003750325,0.0001834366],"domain_scores_gemma":[0.9994605,0.00009920332,0.00008993493,0.0001685948,0.00006955228,0.0001122234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001644374,0.001413316,0.03716502,0.0001704217,0.0003923168,0.00008957594,0.001688142,0.0202068,0.7844547,0.00030761,0.0002255267,0.1522422],"study_design_scores_gemma":[0.005504662,0.002293549,0.08265959,0.0003060504,0.0007197817,0.00001496688,0.002431778,0.7809553,0.1244659,0.00005353198,0.0002623933,0.0003324466],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9320648,0.000009390432,0.0634978,0.001014779,0.00004902299,0.0003795417,0.000008875182,0.00006238074,0.00291337],"genre_scores_gemma":[0.9871079,0.000001017073,0.01148163,0.0008502306,0.0001232321,0.0000174926,0.0001063079,0.00001812641,0.0002940141],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7607485,"threshold_uncertainty_score":0.9994833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197186227022314,"score_gpt":0.3356234091054245,"score_spread":0.3159047864031931,"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."}}