{"id":"W2978560616","doi":"10.1002/hbm.24693","title":"A framework for evaluating correspondence between brain images using anatomical fiducials","year":2019,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Western University; Compute Canada; Royal College of Physicians and Surgeons of Canada; Fondation Brain Canada; Canada First Research Excellence Fund; Health Research","keywords":"Fiducial marker; Computer science; Artificial intelligence; Voxel; Spatial normalization; Protocol (science); Neuroimaging; Set (abstract data type); Template; Computer vision; Neuroanatomy; Pattern recognition (psychology); Neuroscience; Medicine; Psychology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02473707,0.002270208,0.001707949,0.01050489,0.001586568,0.006793113,0.005292408,0.003069208,0.003633359],"category_scores_gemma":[0.06697299,0.001422869,0.002951291,0.005309926,0.00447065,0.005238889,0.008148121,0.003087485,0.001914599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002652932,"about_ca_system_score_gemma":0.005018747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006948937,"about_ca_topic_score_gemma":0.006265204,"domain_scores_codex":[0.9803969,0.008766606,0.001988726,0.003362154,0.004983366,0.0005023993],"domain_scores_gemma":[0.9785029,0.009115561,0.003529205,0.003728567,0.004472437,0.0006512718],"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":[0.0005355259,0.000417732,0.009572428,0.001248315,0.0005460132,0.0004805187,0.001749987,0.1560486,0.02258584,0.2577705,0.01153487,0.5375097],"study_design_scores_gemma":[0.0001141277,0.000935151,0.007808473,0.0005515142,0.0002467199,0.00131646,0.0007381067,0.6781969,0.02089522,0.2541222,0.03472901,0.0003461616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001651843,0.0001848629,0.9959109,0.0001037318,0.00001798939,0.0002436685,0.0002380323,0.0008436074,0.000805361],"genre_scores_gemma":[0.04591114,0.0002069687,0.9511983,0.0000788383,0.00004270711,0.001091388,0.000653732,0.00032667,0.0004904052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02473707,"threshold_uncertainty_score":0.1308237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1345864254049209,"score_gpt":0.4345267640963661,"score_spread":0.2999403386914453,"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."}}