{"id":"W2165733437","doi":"10.1109/tmi.2010.2087390","title":"Segmentation of Intra-Retinal Layers From Optical Coherence Tomography Images Using an Active Contour Approach","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":155,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; McGill University","keywords":"Optical coherence tomography; Segmentation; Retinal; Artificial intelligence; Computer science; Computer vision; Ganglion cell layer; Image segmentation; Retina; Nerve fiber layer; Optics; Pattern recognition (psychology); Physics; Ophthalmology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003406418,0.0002105417,0.0004023696,0.0002814131,0.0001369461,0.00004142974,0.0001592971,0.0001087063,0.0005566897],"category_scores_gemma":[0.0000612542,0.0001829221,0.0002197562,0.0003864694,0.0005938684,0.0002785773,0.00000194551,0.00101392,0.000005570637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003661831,"about_ca_system_score_gemma":0.0001460133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006482179,"about_ca_topic_score_gemma":0.00001952345,"domain_scores_codex":[0.9979724,0.00009890422,0.0004005037,0.000423568,0.0008185494,0.0002860653],"domain_scores_gemma":[0.9987719,0.0001778939,0.0001178725,0.0002958134,0.0001873436,0.0004491536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002952517,0.001109627,0.003388785,0.00005124933,0.0002387018,0.0000671675,0.000600483,0.0002296613,0.8245603,0.000008329866,0.00003402464,0.1694165],"study_design_scores_gemma":[0.002214475,0.0001585435,0.003615657,0.0003063347,0.001053023,0.0002130009,0.003220242,0.1598368,0.8289388,0.00009632721,0.00001634571,0.000330443],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.486646,0.00001674462,0.5120718,0.0004616411,0.0001671126,0.0001118624,0.00002147385,0.00005250601,0.0004509006],"genre_scores_gemma":[0.9533998,0.00001429143,0.04603532,0.0003120888,0.0001509726,0.00001448956,0.00002714973,0.00002493577,0.00002097167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4667538,"threshold_uncertainty_score":0.7459347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01661832786320866,"score_gpt":0.3051264185756198,"score_spread":0.2885080907124111,"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."}}