{"id":"W1906917243","doi":"10.1016/j.media.2015.10.006","title":"Population-based prediction of subject-specific prostate deformation for MR-to-ultrasound image registration","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; University College London Hospitals NHS Foundation Trust; Wellcome Trust; Royal Academy of Engineering; Engineering and Physical Sciences Research Council; Canadian Institutes of Health Research; National Institute for Health and Care Research; NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research; Cancer Research UK","keywords":"Artificial intelligence; Image registration; Percentile; Computer science; Population; Computer vision; Medical imaging; Prostate; Landmark; Magnetic resonance imaging; Ultrasound; Statistical model; Pattern recognition (psychology); Image (mathematics); Mathematics; Statistics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002288402,0.0005159333,0.0007500694,0.0006771632,0.0001494943,0.0004460702,0.0006003074,0.0007488814,0.0007761373],"category_scores_gemma":[0.006131501,0.0004913283,0.001004849,0.0003684172,0.000298086,0.0003515606,0.0004647519,0.0006923589,0.0005260296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004059482,"about_ca_system_score_gemma":0.000843054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003674588,"about_ca_topic_score_gemma":0.005597869,"domain_scores_codex":[0.999454,0.0002468478,0.0000244138,0.0001492442,0.00009582566,0.00002975865],"domain_scores_gemma":[0.9984084,0.001010573,0.0001678243,0.0002210522,0.0001483708,0.00004375587],"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":[0.00023276,0.0001239832,0.01558918,0.00004737487,0.0002059125,0.00007896184,0.0001060837,0.8973897,0.01127433,0.0005018947,0.000632853,0.07381694],"study_design_scores_gemma":[0.00000762363,0.00005925283,0.00449625,0.000003547136,0.00001673639,0.00005943637,0.000008993974,0.9927423,0.002043143,0.0003579921,0.0001916113,0.00001315116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2344585,0.0002486194,0.7628858,0.0001172579,0.0000324372,0.0001202906,0.0003312999,0.001401191,0.0004045242],"genre_scores_gemma":[0.9080415,0.0001867602,0.08959444,0.00006286483,0.00003229457,0.0002214734,0.0009495844,0.0001719514,0.0007391528],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003674588,"threshold_uncertainty_score":0.01210237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01440491051959746,"score_gpt":0.2517029538067149,"score_spread":0.2372980432871174,"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."}}