{"id":"W1484405646","doi":"10.15837/ijccc.2007.1.2333","title":"Deformable Atlases for the Segmentation of Internal Brain Nuclei in Magnetic Resonance Imaging","year":2007,"lang":"en","type":"article","venue":"International Journal of Computers Communications & Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Harvard University; University of Oxford; NIH Clinical Center; National Institutes of Health; Institut national de recherche en informatique et en automatique (INRIA); University of Bern","keywords":"Artificial intelligence; Segmentation; Computer science; Magnetic resonance imaging; Computer vision; Atlas (anatomy); Grey matter; Affine transformation; Image segmentation; Brain atlas; Image registration; Pattern recognition (psychology); Image (mathematics); Anatomy; Radiology; Medicine; Mathematics","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":[0.002136985,0.0001095989,0.0001991155,0.0004154659,0.0000774662,0.0001304665,0.003969236,0.00002791623,0.000008691036],"category_scores_gemma":[0.0003379818,0.00008823079,0.0001411341,0.0002375561,0.0001828379,0.0007925329,0.0003045449,0.0002348512,0.000001375899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001713987,"about_ca_system_score_gemma":0.0001057129,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006555601,"about_ca_topic_score_gemma":0.00003977463,"domain_scores_codex":[0.9979777,0.0001465081,0.001050594,0.0001167477,0.0005327862,0.0001756994],"domain_scores_gemma":[0.9947181,0.003069136,0.0007383514,0.0005531565,0.0008605874,0.00006066421],"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":[0.0001669055,0.0002135813,0.004589673,0.000006316316,0.00005955513,0.00001311343,0.001043228,0.0004351369,0.006057133,0.009883418,0.002375814,0.9751561],"study_design_scores_gemma":[0.009860607,0.0005061645,0.04760843,0.0006986587,0.00005186702,0.0004012263,0.0006700132,0.8913137,0.01976878,0.008048285,0.02072372,0.0003485356],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00176375,0.002461378,0.9848796,0.009893668,0.0005336215,0.000315602,0.000006984935,0.00002244806,0.000122984],"genre_scores_gemma":[0.6862884,0.0001374721,0.3110973,0.002361359,0.00007485793,0.00001480337,0.00000274083,0.000007346664,0.00001577706],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9748076,"threshold_uncertainty_score":0.7375894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01356196748086263,"score_gpt":0.3177810355113612,"score_spread":0.3042190680304986,"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."}}