{"id":"W2076133825","doi":"10.1002/hbm.20780","title":"Comparison of piece‐wise linear, linear, and nonlinear atlas‐to‐patient warping techniques: Analysis of the labeling of subcortical nuclei for functional neurosurgical applications","year":2009,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":73,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Image warping; Artificial intelligence; Computer science; Atlas (anatomy); Brain atlas; Nonlinear system; Pattern recognition (psychology); Linear model; Computer vision; Anatomy; Medicine; Machine learning; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.0004980869,0.0001342863,0.0004408206,0.0003688591,0.0001690026,0.00002151106,0.0004516558,0.00007432457,0.000008897553],"category_scores_gemma":[0.0002891259,0.0001141642,0.0001832777,0.001207351,0.000163249,0.0001065895,0.0001999695,0.0001610703,3.430345e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002434207,"about_ca_system_score_gemma":0.00003763266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006387641,"about_ca_topic_score_gemma":0.000002525156,"domain_scores_codex":[0.9980156,0.00008919755,0.000911882,0.0003557096,0.0004401745,0.0001873906],"domain_scores_gemma":[0.9983036,0.0004133217,0.0004092827,0.0004730395,0.0003020368,0.00009874634],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002659462,0.0006607693,0.004840717,0.0001612745,0.0001532779,6.681731e-7,0.001614604,0.001444499,0.9358137,0.01365548,0.0005478252,0.04108061],"study_design_scores_gemma":[0.0007424041,0.0009391376,0.05142785,0.0003755473,0.0003029152,0.000004312065,0.0002603923,0.6305349,0.3104503,0.001623347,0.002874266,0.0004646507],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08816816,0.00002860197,0.910178,0.0007912789,0.00002165558,0.0006648637,0.00001040978,0.0000869533,0.00005009785],"genre_scores_gemma":[0.5506487,0.000003162526,0.448612,0.0006081666,0.00003625321,0.00005520617,0.00001480124,0.00000778628,0.00001392114],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6290904,"threshold_uncertainty_score":0.4655482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04931572513456597,"score_gpt":0.3499202438290925,"score_spread":0.3006045186945265,"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."}}