{"id":"W4395025790","doi":"10.1162/imag_a_00166","title":"Thalamic nuclei segmentation from T1-weighted MRI: Unifying and benchmarking state-of-the-art methods","year":2024,"lang":"en","type":"article","venue":"Imaging Neuroscience","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Universität Zürich; Eisai; Eidgenössische Technische Hochschule Zürich; Northern California Institute for Research and Education; F. Hoffmann-La Roche; University of Southern California; Pfizer; BioClinica; Biogen; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; National Institute on Aging; Alzheimer's Association","keywords":"Thalamus; Neuroscience; Neuroimaging; Segmentation; Cognition; Human Connectome Project; Brain morphometry; Artificial intelligence; Psychology; Medicine; Magnetic resonance imaging; Computer science; Radiology; Functional connectivity","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008265267,0.002821974,0.001884477,0.008116708,0.001090306,0.004719849,0.00318849,0.003283812,0.00140256],"category_scores_gemma":[0.01822006,0.0008982281,0.002758696,0.002743864,0.001275585,0.002454579,0.002647817,0.001652791,0.001247718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002020923,"about_ca_system_score_gemma":0.002410505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01750298,"about_ca_topic_score_gemma":0.02219611,"domain_scores_codex":[0.9956292,0.0009066279,0.0005917712,0.001284994,0.001267145,0.0003202777],"domain_scores_gemma":[0.9951047,0.002237421,0.0004323242,0.0007256069,0.001274138,0.0002259241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002379036,0.0004244833,0.02873229,0.003167671,0.002404796,0.0006624606,0.001120234,0.267062,0.03675922,0.004957854,0.01232008,0.6400098],"study_design_scores_gemma":[0.0001288134,0.0007032248,0.01276329,0.0004333254,0.0006254009,0.0009532904,0.0004708128,0.9309693,0.03624576,0.006387239,0.01013258,0.000186948],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3456975,0.03027425,0.5870951,0.001565877,0.0009760638,0.001556366,0.005474645,0.01757879,0.009781371],"genre_scores_gemma":[0.5077855,0.00706789,0.4600499,0.0006537555,0.0003276345,0.0005803346,0.01636124,0.003966977,0.003206719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01750298,"threshold_uncertainty_score":0.04371148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04700027216139852,"score_gpt":0.3939438117346635,"score_spread":0.346943539573265,"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."}}