{"id":"W2045355836","doi":"10.1007/s12021-013-9190-5","title":"Deformable Templates Guided Discriminative Models for Robust 3D Brain MRI Segmentation","year":2013,"lang":"en","type":"article","venue":"Neuroinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institute on Aging; Amorfix Life Sciences; Eli Lilly and Company; Bristol-Myers Squibb; National Institute of Biomedical Imaging and Bioengineering; Alzheimer's Drug Discovery Foundation; National Institutes of Health; National Science Foundation","keywords":"Discriminative model; Computer science; Artificial intelligence; Robustness (evolution); Segmentation; Generative model; Pattern recognition (psychology); Neuroimaging; Computer vision; Image segmentation; Template; Generative grammar; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00106753,0.00121328,0.001638443,0.001860802,0.0004378855,0.001601473,0.002394206,0.002120004,0.002226579],"category_scores_gemma":[0.003143606,0.001526631,0.002108787,0.002155929,0.0008567317,0.001396489,0.001767121,0.00220918,0.002064622],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00117597,"about_ca_system_score_gemma":0.001788792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008965748,"about_ca_topic_score_gemma":0.01494655,"domain_scores_codex":[0.9993327,0.0001251413,0.00004869745,0.000183986,0.0002267593,0.00008272062],"domain_scores_gemma":[0.9990399,0.0003724286,0.0001515967,0.0002186914,0.0001707566,0.00004657234],"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":[0.0002796563,0.0001191071,0.0007164122,0.0001993343,0.0001746838,0.0001757269,0.0001364258,0.4384399,0.04653521,0.01100288,0.006525067,0.4956957],"study_design_scores_gemma":[0.000005720701,0.00001892027,0.000196071,0.00001108146,0.00001744685,0.00009300287,0.000009417622,0.9853095,0.008120888,0.004928325,0.001275527,0.00001403813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004219582,0.0002773825,0.9932451,0.0001137127,0.00003365144,0.00003313667,0.000139836,0.00161111,0.0003265233],"genre_scores_gemma":[0.1942372,0.0009638023,0.7960715,0.0004479843,0.000121442,0.000208766,0.001501214,0.001591885,0.004856111],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008965748,"threshold_uncertainty_score":0.01782715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04826608690404589,"score_gpt":0.2909350582236118,"score_spread":0.2426689713195659,"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."}}