{"id":"W3033453827","doi":"10.3389/fnins.2020.00585","title":"Accurate MR Image Registration to Anatomical Reference Space for Diffuse Glioma","year":2020,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Glioma Diagnosis and Treatment","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University College London Hospitals NHS Foundation Trust; Center for Translational Molecular Medicine; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; University College London; KWF Kankerbestrijding; National Institute for Health and Care Research","keywords":"Image registration; Glioma; Landmark; Grey level; Computer science; Artificial intelligence; Glioblastoma; Transformation (genetics); Linear space; Medicine; Computer vision; Mathematics; Image (mathematics); Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00007225609,0.0001291084,0.000217763,0.0001177668,0.00006585558,0.00006338919,0.0001762334,0.00004245761,0.000003154025],"category_scores_gemma":[0.0007938113,0.0001117331,0.00004711284,0.0006541918,0.0000917927,0.0001606043,0.00004736063,0.00009609161,0.000008197158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006761308,"about_ca_system_score_gemma":0.00006663931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001205649,"about_ca_topic_score_gemma":0.000004422605,"domain_scores_codex":[0.9987396,0.00002303678,0.0001958218,0.0005281913,0.0002305683,0.0002828016],"domain_scores_gemma":[0.9993437,0.00003138046,0.000058331,0.0002242261,0.00004714157,0.0002952158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0007895987,0.0007118346,0.04152921,0.00008887787,0.00000633679,0.0006791085,0.0005212717,0.00008650138,0.8387713,0.001450928,0.1127787,0.002586324],"study_design_scores_gemma":[0.01323931,0.005044969,0.4037835,0.0002281787,0.0001417778,0.0001277924,0.000657155,0.1171242,0.3577818,0.0007708645,0.1001298,0.000970652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9467624,0.0000535255,0.0271755,0.02425987,0.0004739932,0.0008854076,0.00002974456,0.00005532123,0.000304279],"genre_scores_gemma":[0.9790915,0.00002628597,0.01800458,0.002674143,0.00004596328,0.00009208843,0.000006373984,0.00001213857,0.0000469006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4809895,"threshold_uncertainty_score":0.4556343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04266204264965891,"score_gpt":0.3057053184834172,"score_spread":0.2630432758337583,"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."}}