{"id":"W2100154344","doi":"10.1007/s10851-008-0071-8","title":"High Angular Resolution Diffusion MRI Segmentation Using Region-Based Statistical Surface Evolution","year":2008,"lang":"en","type":"article","venue":"Journal of Mathematical Imaging and Vision","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Max-Planck-Institut für Kognitions- und Neurowissenschaften; McGill University","keywords":"Diffusion MRI; Segmentation; Angular resolution (graph drawing); Orientation (vector space); Computer science; Artificial intelligence; Tensor (intrinsic definition); Diffusion; Imaging phantom; Image resolution; Fiber; Synthetic data; Pattern recognition (psychology); Surface (topology); Image segmentation; Computer vision; Algorithm; Physics; Mathematics; Magnetic resonance imaging; Materials science; Optics; Geometry","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":[0.0009439763,0.0006771972,0.0009176868,0.001520569,0.0004017601,0.001540038,0.0008373425,0.001137907,0.0009563837],"category_scores_gemma":[0.003081951,0.0006770184,0.0009096481,0.001361053,0.000473081,0.001126833,0.0008752855,0.0009069088,0.0006292378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005797316,"about_ca_system_score_gemma":0.00123106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002986832,"about_ca_topic_score_gemma":0.003438557,"domain_scores_codex":[0.9997075,0.00007014727,0.00002269768,0.00005703183,0.0001172733,0.00002539326],"domain_scores_gemma":[0.9990494,0.0004287931,0.0001179905,0.0001322601,0.0002319264,0.00003962386],"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":[0.0003481331,0.000101427,0.002514414,0.0002946213,0.0002066852,0.0003185072,0.0003934945,0.321868,0.1874422,0.01809005,0.003683696,0.4647387],"study_design_scores_gemma":[0.00000868623,0.00001547778,0.0004606568,0.000006582856,0.0000145514,0.0001123618,0.0000103939,0.980544,0.01378149,0.004115543,0.000915893,0.00001434494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01500646,0.0001580946,0.983414,0.0001295179,0.00001448208,0.00002945426,0.000050759,0.0007958381,0.000401387],"genre_scores_gemma":[0.1938128,0.0002957529,0.8038207,0.00007143201,0.00003091011,0.00006402495,0.0002167866,0.0005442656,0.001143292],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002986832,"threshold_uncertainty_score":0.005938888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05149532651747536,"score_gpt":0.3759047227578678,"score_spread":0.3244093962403925,"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."}}