{"id":"W4200213161","doi":"10.1002/mp.15432","title":"Automated 3D U‐net based segmentation of neonatal cerebral ventricles from 3D ultrasound images","year":2021,"lang":"en","type":"article","venue":"Medical Physics","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Guelph","funders":"","keywords":"Lateral ventricles; Segmentation; Cerebral ventricle; 3D ultrasound; Ventricle; Artificial intelligence; Ultrasound; Computer science; Medicine; Radiology; Anatomy; Cardiology","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":[0.0005971185,0.001342369,0.00101345,0.001374277,0.0004798906,0.001024493,0.001676362,0.00168571,0.001723728],"category_scores_gemma":[0.001122567,0.0006088483,0.001255555,0.0006062433,0.0005120041,0.0006885917,0.0009732799,0.0008067475,0.0008089773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001058997,"about_ca_system_score_gemma":0.001201917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0131297,"about_ca_topic_score_gemma":0.01560056,"domain_scores_codex":[0.9997234,0.00003513592,0.00001691989,0.0001044397,0.00006753817,0.00005258744],"domain_scores_gemma":[0.9996774,0.000106937,0.00004460127,0.0000308807,0.0001031738,0.00003705896],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004846169,0.0001660855,0.006096069,0.00016356,0.0001962419,0.0006006121,0.0001460201,0.5380732,0.0238119,0.001682186,0.007184329,0.4213952],"study_design_scores_gemma":[0.000003834571,0.00002426688,0.0004273448,0.00001288949,0.0000123831,0.00005789155,0.000009969598,0.9951822,0.003300887,0.0005014738,0.000460714,0.000006120982],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1028633,0.001570557,0.8835875,0.0004297881,0.0001924314,0.0001615813,0.0007973739,0.007537676,0.002859731],"genre_scores_gemma":[0.6265041,0.001013149,0.361188,0.0006064516,0.0001281694,0.0002648582,0.003096399,0.0004908198,0.006708091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0131297,"threshold_uncertainty_score":0.0261066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01090212038690359,"score_gpt":0.2557552847164312,"score_spread":0.2448531643295276,"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."}}