{"id":"W2152896605","doi":"10.1007/978-3-642-04271-3_79","title":"Intra-retinal Layer Segmentation in Optical Coherence Tomography Using an Active Contour Approach","year":2009,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":98,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; McGill University; Michael Smith Health Research BC; California HIV/AIDS Research Program","keywords":"Optical coherence tomography; Computer science; Artificial intelligence; Segmentation; Retinal; Computer vision; Pattern recognition (psychology); Active contour model; Glaucoma; Contrast (vision); Image segmentation; Optics; Physics; Ophthalmology","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":[0.0004250536,0.0001351175,0.0002315642,0.0004153979,0.00007334707,0.0001004903,0.0002040569,0.00005117443,0.000005149336],"category_scores_gemma":[0.00006987416,0.0001102489,0.00004692248,0.001675844,0.0002632667,0.0002988397,0.00002964122,0.0003007355,9.075141e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078394,"about_ca_system_score_gemma":0.0001052194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007223179,"about_ca_topic_score_gemma":0.00001048493,"domain_scores_codex":[0.9985574,0.00005711931,0.0002020614,0.0004807844,0.0003836302,0.0003190027],"domain_scores_gemma":[0.9994531,0.00006311149,0.00005471881,0.0002128942,0.00009988075,0.0001163395],"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":[0.0001332141,0.0006064802,0.06777634,0.00002196989,0.00001057223,0.0001241018,0.003003202,0.07935894,0.1812363,0.00003325981,0.000001279481,0.6676943],"study_design_scores_gemma":[0.000534985,0.0003082246,0.09941982,0.0001148704,0.00002068469,0.0001192125,0.00002267658,0.8276199,0.07087477,0.0008118618,4.817409e-7,0.0001525874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4889046,0.0000176791,0.5106131,0.0002924387,0.00003081731,0.00008710494,2.354639e-7,0.00001452987,0.0000395087],"genre_scores_gemma":[0.7320626,0.000001068503,0.2671351,0.0007100702,0.00008337762,0.000001634006,0.000002887984,0.000003017806,2.504657e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7482609,"threshold_uncertainty_score":0.4495819,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0259654486208485,"score_gpt":0.315407472745822,"score_spread":0.2894420241249736,"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."}}