{"id":"W2022935832","doi":"10.1016/j.neuroimage.2004.09.022","title":"Brain structural mapping using a novel hybrid implicit/explicit framework based on the level-set method","year":2004,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":72,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"National Center for Research Resources; National Institutes of Health; National Institute of Biomedical Imaging and Bioengineering; University of California, Los Angeles; U.S. Department of Energy","keywords":"Image warping; Computer science; Landmark; Artificial intelligence; Matching (statistics); Set (abstract data type); Similarity (geometry); Tensor (intrinsic definition); Feature (linguistics); Algorithm; Pattern recognition (psychology); Image (mathematics); Point (geometry); Orientation (vector space); Computer vision; Mathematics; Geometry","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":[0.0007693069,0.0008329371,0.001316415,0.0009111211,0.0005127078,0.001610853,0.003273598,0.00172634,0.002771552],"category_scores_gemma":[0.001587401,0.0009104598,0.001254412,0.0007638216,0.0006298543,0.001896694,0.00187917,0.002028785,0.0008972415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004942969,"about_ca_system_score_gemma":0.001278202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002594158,"about_ca_topic_score_gemma":0.004201805,"domain_scores_codex":[0.9995426,0.00009766599,0.00002304046,0.00005933523,0.0002469441,0.00003055359],"domain_scores_gemma":[0.9995178,0.0001943777,0.00004671501,0.00009333238,0.0001031101,0.00004469326],"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":[0.0001617397,0.0001589854,0.0006048057,0.000387384,0.0003551379,0.0002331692,0.0001866221,0.5534458,0.0538159,0.07637873,0.00310934,0.3111624],"study_design_scores_gemma":[0.000008386623,0.00001451624,0.00006800098,0.000006614136,0.00001623956,0.0000484073,0.000003413597,0.9901146,0.00281511,0.005879171,0.001013607,0.00001194768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001272527,0.00004566179,0.9981418,0.00003602962,0.000009616015,0.000009507521,0.00001091866,0.0001915231,0.0002824551],"genre_scores_gemma":[0.08315642,0.0002407059,0.9132762,0.00008303102,0.0000490156,0.0001357372,0.0001097827,0.0004402465,0.002508927],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003273598,"threshold_uncertainty_score":0.009271801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003337650808458,"score_gpt":0.3620297873475495,"score_spread":0.2616960222667036,"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."}}