{"id":"W2003685825","doi":"10.1118/1.4888094","title":"SU‐E‐J‐42: Customized Deformable Image Registration Using Open‐Source Software SlicerRT","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Imaging phantom; Image registration; Artificial intelligence; Computer vision; Ground truth; Computer science; Hounsfield scale; Voxel; Software; Medical imaging; Nuclear medicine; Image (mathematics); Computed tomography; Medicine","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":[0.0006492923,0.0001762177,0.0003550945,0.00003160908,0.0001267506,0.0001517506,0.0004423343,0.0001095755,0.000268185],"category_scores_gemma":[0.0005988851,0.000153448,0.0001028716,0.0002754415,0.0001186205,0.0003264834,0.0001051665,0.0003821806,0.0001317836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006462065,"about_ca_system_score_gemma":0.00005826556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001900281,"about_ca_topic_score_gemma":0.000005533447,"domain_scores_codex":[0.9984427,0.00005651057,0.0003183193,0.0002109041,0.000629086,0.000342447],"domain_scores_gemma":[0.9990885,0.0001185303,0.00005902449,0.000337251,0.00005192027,0.00034483],"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":[0.00003823399,0.0003657104,0.002197073,0.0008290815,0.0004476196,0.00005075114,0.0008480917,0.04205168,0.005955257,0.001030881,0.09155511,0.8546305],"study_design_scores_gemma":[0.001094798,0.00000982791,0.00002756727,0.000138023,0.00008613924,0.000007654839,0.0000248607,0.9677504,0.002671598,0.001387239,0.02653334,0.0002685573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02957835,0.00004444514,0.9626733,0.0002723534,0.0002197488,0.00008586391,0.00000242997,0.0003791707,0.006744355],"genre_scores_gemma":[0.9870344,0.00002887228,0.01007778,0.0008749215,0.001068722,0.00001431435,0.00007505789,0.00007462166,0.0007512553],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9574561,"threshold_uncertainty_score":0.6257427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01494038228750218,"score_gpt":0.2584891197890906,"score_spread":0.2435487375015885,"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."}}