{"id":"W2783775739","doi":"10.1016/j.media.2018.09.002","title":"Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; Engineering and Physical Sciences Research Council; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; University of California, San Diego; National Institutes of Health; Genentech; National Institute of Neurological Disorders and Stroke; IXICO; Servier; Eisai; DoD Alzheimer's Disease Neuroimaging Initiative; Pfizer; Biogen; BioClinica; National Institute of Mental Health; Neurosciences Research Foundation; National Center for Research Resources; F. Hoffmann-La Roche; University of Southern California; Wellcome Trust; National Institute of Diabetes and Digestive and Kidney Diseases; Synarc; Medpace; European Research Council; Northern California Institute for Research and Education; Massachusetts General Hospital; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Novartis Pharmaceuticals Corporation; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Artificial intelligence; Image registration; Computer science; Probabilistic logic; Computer vision; Rigid transformation; Robustness (evolution); Mutual information; Pattern recognition (psychology); Metric (unit); Inference; Affine transformation; Bayesian inference; Real-time MRI; Bayesian probability; Image (mathematics); Magnetic resonance imaging; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008583143,0.00009582009,0.0002945113,0.0001923614,0.0001148801,0.00005315591,0.0001794792,0.00008127541,0.00005345007],"category_scores_gemma":[0.001758776,0.00007715292,0.00008588875,0.0004220397,0.0006118488,0.0001995359,0.0001199865,0.00006433065,4.469036e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004806805,"about_ca_system_score_gemma":0.00007593886,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008979181,"about_ca_topic_score_gemma":0.00004492567,"domain_scores_codex":[0.9985542,0.00008830945,0.0004026218,0.0003621971,0.0004459247,0.0001467296],"domain_scores_gemma":[0.9990189,0.0001844489,0.0001608642,0.0002667342,0.0001795619,0.0001894612],"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":[0.0002056513,0.004349531,0.0025826,0.001894368,0.005806371,0.0001594947,0.01260186,0.001251479,0.2538709,0.04694279,0.01702637,0.6533086],"study_design_scores_gemma":[0.0001263723,0.00007458223,0.0001657825,0.00002209528,0.0003890656,0.000009801151,0.00002499703,0.9828198,0.01298306,0.003296251,0.000007959025,0.00008025944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01133859,0.00003885872,0.9871029,0.001179646,0.0000173493,0.0001842434,0.000004840623,0.00005749889,0.00007611708],"genre_scores_gemma":[0.4083563,0.00002761127,0.5912814,0.0002164432,0.00002609397,0.00004747157,0.000002598552,0.000003387963,0.0000386871],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9815683,"threshold_uncertainty_score":0.3146205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04816367954821354,"score_gpt":0.3234920483452383,"score_spread":0.2753283687970248,"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."}}