{"id":"W2029367007","doi":"10.1117/12.2032197","title":"Texture based segmentation method to detect atherosclerotic plaque from optical tomography images","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Manitoba","funders":"","keywords":"Optical coherence tomography; Segmentation; Image segmentation; Artificial intelligence; Vulnerable plaque; Computer science; Computer vision; Texture (cosmology); Image texture; Medicine; Biomedical engineering; Radiology; Pathology; Image (mathematics)","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":[0.0004129632,0.0004699926,0.0005811213,0.00174909,0.0003187997,0.000637364,0.0006576314,0.0006530928,0.001232608],"category_scores_gemma":[0.001049513,0.0003885668,0.0006098758,0.00106847,0.0002701511,0.0004646351,0.0002998672,0.000512014,0.0006299645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004629788,"about_ca_system_score_gemma":0.0005391995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002416119,"about_ca_topic_score_gemma":0.002668864,"domain_scores_codex":[0.9995868,0.00004016448,0.00002980453,0.00007513056,0.0002291142,0.00003904214],"domain_scores_gemma":[0.999496,0.0001302542,0.00006713587,0.00005316337,0.0002254128,0.00002812199],"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":[0.0002446519,0.0001347602,0.001943522,0.0002009187,0.00008223226,0.0002606394,0.0001433542,0.02071483,0.4875276,0.001936414,0.003413781,0.4833972],"study_design_scores_gemma":[0.00005951724,0.0001845353,0.008213856,0.00002385914,0.0001010119,0.001159661,0.00005877925,0.7710276,0.2066584,0.001755699,0.01068899,0.00006802814],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03113491,0.0002430837,0.9656662,0.00009834459,0.00006868651,0.00008552927,0.0001066978,0.001587668,0.001008824],"genre_scores_gemma":[0.1994724,0.0004285461,0.7961048,0.0001096528,0.00009000715,0.0001377887,0.0003786768,0.0003422742,0.002935831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002416119,"threshold_uncertainty_score":0.004804134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008187948595570704,"score_gpt":0.2365261217912721,"score_spread":0.2283381731957014,"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."}}