{"id":"W4391264577","doi":"10.1016/j.media.2024.103093","title":"Neural deformation fields for template-based reconstruction of cortical surfaces from MRI","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Segmentation; Polygon mesh; Computer vision; Surface (topology); Flow (mathematics); Surface reconstruction; Pattern recognition (psychology); Mathematics; Geometry; Computer graphics (images)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001665743,0.00007789914,0.0002543063,0.0001638526,0.000042986,0.00001840477,0.00006475837,0.00008275804,0.0004542615],"category_scores_gemma":[0.0002449645,0.0000609037,0.0002367668,0.0005321166,0.000117466,0.00009616333,0.00001391421,0.0001868927,0.000005135959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001969307,"about_ca_system_score_gemma":0.00005204554,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007723147,"about_ca_topic_score_gemma":0.00001576379,"domain_scores_codex":[0.9990894,0.00002207188,0.0003308638,0.000202938,0.000246812,0.0001079435],"domain_scores_gemma":[0.9992487,0.0003089829,0.00005118637,0.0002005701,0.00008758706,0.000102988],"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":[0.001086106,0.001375943,0.1061402,0.002213348,0.005718355,0.000280377,0.0006913676,0.003426623,0.1380943,0.002620771,0.049071,0.6892815],"study_design_scores_gemma":[0.0002609713,0.00007429929,0.003726832,0.00007557591,0.001453342,0.00001016636,0.00002877739,0.9783562,0.01367757,0.0008422136,0.001421563,0.00007243157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2396963,0.00009609047,0.7539157,0.005800814,0.00006211022,0.0001573749,0.00004470537,0.0001410241,0.00008590827],"genre_scores_gemma":[0.952804,0.00004123355,0.04638382,0.0003358134,0.0001071463,0.00003895123,0.0002434666,0.000009134903,0.00003644547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9749296,"threshold_uncertainty_score":0.4973845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03829917376689692,"score_gpt":0.3651151343577,"score_spread":0.3268159605908031,"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."}}