{"id":"W3046471534","doi":"10.1101/2020.08.03.197384","title":"TractoFlow-ABS (Atlas-Based Segmentation)","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"White matter; Tractography; Atlas (anatomy); Diffusion MRI; Hyperintensity; Segmentation; Computer science; Artificial intelligence; Anatomy; Pattern recognition (psychology); Magnetic resonance imaging; Biology; Medicine; Radiology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001395273,0.002155295,0.001032557,0.002379307,0.001185661,0.002598852,0.002383126,0.001567004,0.02666398],"category_scores_gemma":[0.00391874,0.001384435,0.002373762,0.001793522,0.0007680861,0.00165743,0.003395799,0.00163492,0.01251586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001204551,"about_ca_system_score_gemma":0.002024506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008731163,"about_ca_topic_score_gemma":0.0116748,"domain_scores_codex":[0.9991729,0.0000866685,0.00007475426,0.0002500781,0.0003149881,0.0001006575],"domain_scores_gemma":[0.9988294,0.0002311778,0.0001158611,0.0004479577,0.0002912143,0.00008432823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00167755,0.0002342614,0.004522757,0.001170437,0.0009315532,0.0006855475,0.0007432966,0.07532786,0.09764274,0.03593438,0.1706989,0.6104308],"study_design_scores_gemma":[0.0001380549,0.0002291164,0.003009558,0.0001160512,0.0001008652,0.000870186,0.00008448799,0.6508784,0.1714126,0.02847894,0.1445043,0.0001773898],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01293216,0.0002535705,0.8204689,0.0001750206,0.0002479674,0.0002687495,0.004624539,0.1562608,0.004768331],"genre_scores_gemma":[0.06603289,0.000200696,0.8839107,0.000185962,0.00007451468,0.0003098077,0.01040683,0.03091382,0.007964916],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.02666398,"threshold_uncertainty_score":0.0891999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05107801343008658,"score_gpt":0.3052864410448513,"score_spread":0.2542084276147647,"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."}}