{"id":"W118997998","doi":"10.1007/978-3-642-23629-7_68","title":"Random Walks for Deformable Image Registration","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Image registration; Random walk; Computer vision; Artificial intelligence; Computer graphics (images); Image (mathematics); Mathematics; Statistics","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":[0.001329953,0.0001419742,0.0001624258,0.0002489577,0.0001788188,0.0002693088,0.001568331,0.00005979738,0.00001443994],"category_scores_gemma":[0.0003182747,0.0001187164,0.00005119271,0.0009493372,0.0003409342,0.001634423,0.000257306,0.0001449407,0.000009808031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008524891,"about_ca_system_score_gemma":0.0001809803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005297091,"about_ca_topic_score_gemma":0.00002046471,"domain_scores_codex":[0.9982249,0.00004333391,0.0003186161,0.000566334,0.0004254853,0.0004213693],"domain_scores_gemma":[0.9987571,0.0002161786,0.0001222099,0.000599398,0.000188754,0.0001163969],"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":[0.00002236511,0.00007544745,0.000126413,0.00002579582,0.000002759675,0.00001265742,0.002473375,0.0003524734,0.01523942,0.001121626,0.0001210306,0.9804266],"study_design_scores_gemma":[0.000605797,0.0001479288,0.000349376,0.00002674701,0.000001614832,0.00001895826,6.131232e-7,0.5427825,0.4191902,0.03669217,0.00002447889,0.0001596366],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007952081,0.00002426994,0.9974281,0.0003586026,0.0005071916,0.00046585,7.799061e-7,0.000238334,0.0001816339],"genre_scores_gemma":[0.2544903,0.000003566405,0.7443805,0.001020558,0.0000559598,0.00004123425,0.000001212587,0.000004505832,0.00000216031],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.980267,"threshold_uncertainty_score":0.4841113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02408593641414553,"score_gpt":0.2815436391959181,"score_spread":0.2574577027817726,"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."}}