{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006559664,0.000792205,0.0007871602,0.002324319,0.0005441261,0.001000224,0.001047229,0.001162053,0.0009614071],"category_scores_gemma":[0.001115432,0.000531573,0.0009912627,0.00127349,0.0004699877,0.0009813112,0.0005238381,0.0006988285,0.0006325183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005475137,"about_ca_system_score_gemma":0.0008446854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003835585,"about_ca_topic_score_gemma":0.003179809,"domain_scores_codex":[0.9994536,0.00005660526,0.00003946539,0.0001604623,0.0002488046,0.00004107111],"domain_scores_gemma":[0.9994488,0.0001783195,0.00007355717,0.00006883491,0.0002026069,0.00002777942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002160964,0.00008839229,0.001872523,0.0003158761,0.0001143161,0.0002588759,0.0002822948,0.05400883,0.1528849,0.005363895,0.003279415,0.7813146],"study_design_scores_gemma":[0.00003906008,0.0001023946,0.002127027,0.00004297646,0.00008980807,0.0006507254,0.00004324265,0.9079871,0.07749332,0.002164071,0.009185928,0.0000743414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007355577,0.0003957647,0.9905048,0.0000620418,0.00006871606,0.00005865183,0.00003507973,0.0006178743,0.0009014098],"genre_scores_gemma":[0.1318986,0.0008817057,0.8629794,0.0001172873,0.0001208359,0.0001208131,0.000216943,0.0002000461,0.00346433],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003835585,"threshold_uncertainty_score":0.007626534,"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."}}