{"id":"W2208574443","doi":"10.1016/j.neuroimage.2015.05.099","title":"3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets","year":2015,"lang":"en","type":"article","venue":"NeuroImage","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Canadian Institutes of Health Research; Academic Medical Organization of Southwestern Ontario","keywords":"Medicine; Lateral ventricles; Ventricle; Segmentation; Hydrocephalus; Cerebral ventricle; Radiology; Cardiology; Internal medicine; Artificial intelligence; Pathology; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001398424,0.0002055592,0.0002516858,0.0001960767,0.00005725873,0.00003482051,0.00009340679,0.00007088915,0.00005989727],"category_scores_gemma":[0.000206282,0.0001684862,0.00004931703,0.0005602604,0.0000606028,0.0003377484,0.0000510163,0.000245573,0.0000512512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006749234,"about_ca_system_score_gemma":0.0001009286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001732796,"about_ca_topic_score_gemma":0.00002318889,"domain_scores_codex":[0.99843,0.000106079,0.0003160751,0.0004103887,0.0003831624,0.0003542877],"domain_scores_gemma":[0.9992492,0.00006483274,0.0001374391,0.0002237539,0.0001075065,0.0002172761],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008330668,0.005271819,0.685616,0.0003278086,0.00004430902,0.00470045,0.002647323,0.00153166,0.2110047,0.0000104524,0.000245838,0.08026897],"study_design_scores_gemma":[0.01902096,0.002698435,0.9308485,0.00003905453,0.0001523805,0.0002944979,0.0001222084,0.04202622,0.004317521,0.00005476923,0.00009117646,0.00033421],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975126,0.0001993891,0.000914616,0.0002320852,0.0001211594,0.0007653923,0.00004633595,0.00007874086,0.0001296519],"genre_scores_gemma":[0.9956805,0.00002249836,0.003132808,0.0008805847,0.00005490407,0.00001759047,0.0001170786,0.00003135311,0.00006275454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2452326,"threshold_uncertainty_score":0.6870666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08719925988263415,"score_gpt":0.3450521403921973,"score_spread":0.2578528805095632,"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."}}