{"id":"W2026105938","doi":"10.1016/j.neuroimage.2013.07.053","title":"Image registration of ex-vivo MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens","year":2013,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; Robarts Clinical Trials","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Magnetic resonance imaging; Histology; Ex vivo; Image registration; Hippocampal formation; Temporal lobe; Radiology; Surgical planning; Histopathology; Neuronavigation; Medicine; Computer science; Nuclear medicine; Epilepsy; Pathology; Artificial intelligence; In vivo; Biology; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002082071,0.0001271059,0.0002444077,0.0001493868,0.000034622,0.00005198943,0.0003297865,0.00006034126,0.0002335068],"category_scores_gemma":[0.0002324264,0.0001248573,0.00004296346,0.000271383,0.0002547207,0.0005325199,0.0001626636,0.0001322044,0.00003436277],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002521181,"about_ca_system_score_gemma":0.00005097925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002062237,"about_ca_topic_score_gemma":0.00001289612,"domain_scores_codex":[0.998462,0.0001304506,0.0004988417,0.0003807727,0.0003281898,0.0001998122],"domain_scores_gemma":[0.9988704,0.00010996,0.0002129775,0.0004598062,0.0001791288,0.0001677591],"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":[0.00001677138,0.0001288164,0.001132837,0.00005027683,0.000006527305,0.00002954612,0.00036207,0.000001144017,0.9635853,0.001819101,0.02480676,0.008060821],"study_design_scores_gemma":[0.001108938,0.001796485,0.1003068,0.00005171299,0.00002707664,0.0001785183,0.00009892447,0.01122322,0.8766579,0.006708637,0.001385143,0.000456653],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1786171,0.00001194451,0.8157766,0.001991947,0.0001672454,0.0004730343,0.00000386698,0.0001244672,0.00283375],"genre_scores_gemma":[0.5939643,0.00000932219,0.4049652,0.0006608184,0.00004097871,0.00002400997,0.000002851424,0.00001131754,0.0003212005],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4153472,"threshold_uncertainty_score":0.5091532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02854202608416044,"score_gpt":0.2693858480444011,"score_spread":0.2408438219602407,"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."}}