{"id":"W2980934859","doi":"10.1002/hbm.24811","title":"Hippocampal segmentation for brains with extensive atrophy using three‐dimensional convolutional neural networks","year":2019,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":83,"is_retracted":false,"has_abstract":true,"ca_institutions":"Heart and Stroke Foundation; University of Toronto; Ontario Brain Institute; Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; H. Lundbeck A/S; National Institute on Aging; Fujirebio Europe; Pfizer; Novartis Pharmaceuticals Corporation; AbbVie; Takeda Pharmaceutical Company; Bristol-Myers Squibb; Eli Lilly and Company; Servier; GE Healthcare; BioClinica; Norman Cousins Center for Psychoneuroimmunology; Alzheimer's Drug Discovery Foundation; Biogen","keywords":"Computer science; Atrophy; Convolutional neural network; Artificial intelligence; Segmentation; Sørensen–Dice coefficient; Dementia; Pattern recognition (psychology); Correlation; Medicine; Pathology; Disease; Image segmentation; Mathematics","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":[0.0009958716,0.001374626,0.0006415234,0.001638446,0.0006150954,0.001120481,0.00132564,0.001202722,0.001447702],"category_scores_gemma":[0.002393922,0.0007469765,0.001547073,0.0007991334,0.0006022701,0.0007037679,0.001308038,0.001295522,0.0008299841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001362069,"about_ca_system_score_gemma":0.002118263,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02902571,"about_ca_topic_score_gemma":0.06242423,"domain_scores_codex":[0.9996924,0.00004302141,0.0000234181,0.000122148,0.00006711846,0.00005181208],"domain_scores_gemma":[0.9994778,0.0001812743,0.00009001172,0.00011769,0.0001052037,0.00002812054],"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":[0.0006559203,0.0001833825,0.01896233,0.0004889496,0.0007642757,0.0006342214,0.0005376041,0.3622534,0.03786106,0.004629198,0.0215673,0.5514625],"study_design_scores_gemma":[0.00002940662,0.00005572589,0.003521849,0.00007904626,0.00009354357,0.0003223788,0.00006070891,0.9681646,0.01882217,0.005013363,0.00380004,0.00003714956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2704568,0.00420586,0.6964542,0.001106796,0.0002666021,0.0003542148,0.003570483,0.01955581,0.004029145],"genre_scores_gemma":[0.6374479,0.001364156,0.3461639,0.0006019421,0.00009485932,0.0002221419,0.007436978,0.001373468,0.005294735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02902571,"threshold_uncertainty_score":0.05771351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06268423052243269,"score_gpt":0.2818773216889248,"score_spread":0.2191930911664921,"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."}}