{"id":"W4281621674","doi":"10.3389/fnins.2022.887634","title":"FetalGAN: Automated Segmentation of Fetal Functional Brain MRI Using Deep Generative Adversarial Learning and Multi-Scale 3D U-Net","year":2022,"lang":"en","type":"article","venue":"Frontiers in Neuroscience","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Heart, Lung, and Blood Institute; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Artificial intelligence; Computer science; Segmentation; Convolutional neural network; Sørensen–Dice coefficient; Pattern recognition (psychology); Deep learning; Preprocessor; Voxel; Magnetic resonance imaging; Artificial neural network; Ground truth; Image segmentation; Medicine; Radiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001995366,0.0001074644,0.0001909289,0.0001676751,0.0002427573,0.00001149807,0.00006855754,0.00003341833,0.00004119591],"category_scores_gemma":[0.0001506886,0.0001024159,0.00003974482,0.0005561846,0.0002411958,0.0001161513,0.0001212958,0.0002812273,3.348806e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004726589,"about_ca_system_score_gemma":0.00006156587,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002417977,"about_ca_topic_score_gemma":0.000003025811,"domain_scores_codex":[0.9986914,0.000187057,0.0002139791,0.0003652615,0.0003438483,0.0001984655],"domain_scores_gemma":[0.9997005,0.00004335654,0.0001058522,0.00006039045,0.00002114234,0.00006872573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009191195,0.0004819739,0.4829582,0.0000564655,0.00001063525,0.0001009037,0.001097093,0.1505527,0.3568556,0.00001208188,0.001535142,0.0054201],"study_design_scores_gemma":[0.001665592,0.000551035,0.08594579,0.000002484777,0.00002017923,0.0000471734,0.0004621173,0.9103918,0.0003316551,0.00001839861,0.0004672389,0.00009650266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9198909,0.0002132279,0.0774655,0.0003592937,0.001522105,0.00035644,0.00001570159,0.00007389672,0.0001029722],"genre_scores_gemma":[0.9701421,0.00004241564,0.02855068,0.000914506,0.00004506837,0.00001747356,0.00002327283,0.00001121417,0.0002533021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7598392,"threshold_uncertainty_score":0.41764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0192596658595514,"score_gpt":0.2672898538335878,"score_spread":0.2480301879740364,"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."}}