{"id":"W3056601423","doi":"10.1016/j.mex.2020.101023","title":"Semi-automated segmentation of the lateral periventricular regions using diffusion magnetic resonance imaging","year":2020,"lang":"en","type":"article","venue":"MethodsX","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Center for Advancing Translational Sciences; National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; March of Dimes Prematurity Research Center Ohio Collaborative; Doris Duke Charitable Foundation; Child Neurology Foundation; Cerebral Palsy International Research Foundation; Dana Foundation; National Institutes of Health; March of Dimes Foundation","keywords":"Cerebrospinal fluid; Magnetic resonance imaging; Diffusion MRI; Biomedical engineering; SIGNAL (programming language); Ventricular system; Hydrocephalus; White matter; Voxel; Medicine; Materials science; Computer science; Pathology; 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":[0.001203797,0.0009106576,0.0006313249,0.002144164,0.0005720477,0.002254108,0.001130483,0.001139606,0.002962009],"category_scores_gemma":[0.00271458,0.0006863238,0.0009493098,0.0009545403,0.0005065922,0.0008075216,0.001156208,0.000765695,0.001980936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004927368,"about_ca_system_score_gemma":0.002188468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004248486,"about_ca_topic_score_gemma":0.007716562,"domain_scores_codex":[0.9994451,0.00008721122,0.00007438011,0.0001742315,0.0001532445,0.00006586],"domain_scores_gemma":[0.9992657,0.0002472739,0.0001423348,0.0001201858,0.0001895119,0.00003495533],"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":[0.0003987056,0.0001620241,0.005551119,0.000776692,0.0002220374,0.0007448592,0.001056759,0.0400099,0.2330716,0.005978466,0.008536141,0.7034917],"study_design_scores_gemma":[0.0001116986,0.0003137045,0.0160785,0.0002640365,0.0001829901,0.002476244,0.000595675,0.7314199,0.1985904,0.01200604,0.03777445,0.0001863602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03934645,0.00045439,0.9519257,0.0001990495,0.00006114604,0.000367528,0.0006147083,0.005298274,0.001732791],"genre_scores_gemma":[0.09997215,0.0003912196,0.8951786,0.00009121398,0.00003273301,0.0002643893,0.0009500952,0.0009948475,0.002124872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004248486,"threshold_uncertainty_score":0.009908974,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02892943696976811,"score_gpt":0.3118736157767871,"score_spread":0.2829441788070189,"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."}}