{"id":"W135546504","doi":"10.1007/978-3-642-33454-2_47","title":"Efficient Global Optimization Based 3D Carotid AB-LIB MRI Segmentation by Simultaneously Evolving Coupled Surfaces","year":2012,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials; Western University","funders":"","keywords":"Segmentation; Computer science; Magnetic resonance imaging; Consistency (knowledge bases); Relaxation (psychology); Image segmentation; Artificial intelligence; Lumen (anatomy); Repeatability; Carotid arteries; Computer vision; Radiology; Mathematics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001361252,0.0002868367,0.0002420097,0.0002183266,0.0002972767,0.0005343021,0.001429195,0.0001074443,0.00004350586],"category_scores_gemma":[0.0003793451,0.000264351,0.00004830966,0.002414571,0.0003507591,0.0009561335,0.0003720949,0.0002116534,0.000014268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005831063,"about_ca_system_score_gemma":0.00024344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001147306,"about_ca_topic_score_gemma":0.000008924583,"domain_scores_codex":[0.9965355,0.0001759687,0.0004583205,0.0007838376,0.001261013,0.0007852966],"domain_scores_gemma":[0.9980686,0.0005302645,0.0002176815,0.0006062269,0.0002792995,0.0002979648],"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":[0.000002789697,0.0001265051,0.002972131,0.00001409063,0.000002550331,0.000003691315,0.0005008233,0.9584964,0.005792471,0.000009520189,0.00005233776,0.03202671],"study_design_scores_gemma":[0.0003573821,0.00008268527,0.0004422424,0.00004868552,0.000004114718,0.00001325174,0.000001888051,0.953161,0.04556286,0.00004545545,0.000002837053,0.0002775783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007399182,0.0002017755,0.9900106,0.0004320612,0.0009517096,0.000559537,0.000005107219,0.0004201679,0.00001992797],"genre_scores_gemma":[0.4808453,0.000003333805,0.5181256,0.0009501569,0.00004855037,0.00001302221,0.000007459146,0.000006140174,4.091988e-7],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4734462,"threshold_uncertainty_score":0.9999809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00796063068306078,"score_gpt":0.2665269464248773,"score_spread":0.2585663157418165,"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."}}