{"id":"W2110283854","doi":"10.1109/ccece.2010.5575182","title":"A cellular automata based semi-automatic algorithm for segmentation of choroidal blood vessels from ultrahigh resolution optical coherence images of rat retina","year":2010,"lang":"en","type":"article","venue":"","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Optical coherence tomography; Blood flow; Computer science; Retina; Retinal; Choroid; Segmentation; Diabetic retinopathy; Artificial intelligence; Computer vision; Speckle pattern; Image segmentation; Contrast (vision); Biomedical engineering; Ophthalmology; Medicine; Optics; Radiology; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000395963,0.0005733176,0.0006153208,0.0006697949,0.0006606064,0.0007685752,0.0007395063,0.0008631895,0.0008397279],"category_scores_gemma":[0.001374225,0.0003829656,0.0006726919,0.0003487946,0.0006185978,0.0004207104,0.0005242347,0.0004733239,0.0002320191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009062013,"about_ca_system_score_gemma":0.001122682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01047096,"about_ca_topic_score_gemma":0.0121071,"domain_scores_codex":[0.9997429,0.00005238454,0.00002538648,0.00008279326,0.00007081385,0.00002570671],"domain_scores_gemma":[0.9993231,0.0003390973,0.00007378713,0.0000618829,0.0001695733,0.00003265898],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002136113,0.00008874905,0.002233095,0.0002116049,0.0000992758,0.0002556769,0.0003931641,0.6024623,0.06679073,0.004823431,0.001219388,0.3212089],"study_design_scores_gemma":[0.000008330107,0.00002971141,0.0002656902,0.00000710634,0.00001081866,0.00003924084,0.00001421696,0.9926974,0.005581468,0.0007986758,0.0005353259,0.00001193418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0295145,0.0001016434,0.9684216,0.00007307923,0.00002912524,0.00005381458,0.00003557083,0.00106575,0.0007050295],"genre_scores_gemma":[0.361218,0.0001144111,0.6366834,0.00005383894,0.000009866368,0.0002627279,0.000109403,0.00007314055,0.001475272],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01047096,"threshold_uncertainty_score":0.02082002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006945935025840499,"score_gpt":0.2238840853689348,"score_spread":0.2169381503430943,"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."}}