{"id":"W2071054694","doi":"10.1118/1.3284368","title":"Reconstruction of 3D lung models from 2D planning data sets for Hodgkin's lymphoma patients using combined deformable image registration and navigator channels","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Princess Margaret Cancer Centre; University Health Network","funders":"Canadian Institutes of Health Research; Free To Breathe","keywords":"Sørensen–Dice coefficient; Radiation treatment planning; Medicine; Population; Radiology; Lung; Image registration; Lung cancer; Radiography; Nuclear medicine; Radiation therapy; Computer science; Artificial intelligence; Image segmentation; Segmentation; Pathology; Image (mathematics); Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007261027,0.0004740359,0.0003680003,0.001097685,0.0001689581,0.0007466858,0.000464246,0.0004407834,0.001221323],"category_scores_gemma":[0.002115533,0.0005835501,0.001022187,0.0005049242,0.0001961793,0.0003153804,0.000540413,0.000552672,0.0003726929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006063826,"about_ca_system_score_gemma":0.0007637125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004466259,"about_ca_topic_score_gemma":0.005046968,"domain_scores_codex":[0.9996992,0.00008078764,0.00002533562,0.00007005062,0.0001047624,0.00001981158],"domain_scores_gemma":[0.9995639,0.0001907913,0.00005818561,0.000116864,0.00005440051,0.00001586186],"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":[0.0006735797,0.0002262758,0.05491693,0.0001998902,0.0002338815,0.001075787,0.000564341,0.6262568,0.04795441,0.001455243,0.001356456,0.2650864],"study_design_scores_gemma":[0.0000466839,0.000218222,0.02437455,0.00002144037,0.00007282864,0.00178121,0.0001442198,0.9424477,0.02706396,0.001169978,0.002589582,0.00006954562],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5539909,0.0004447221,0.4402659,0.0001928656,0.00002724051,0.0002378175,0.001004998,0.002182622,0.001653062],"genre_scores_gemma":[0.8776698,0.0002313234,0.1199404,0.00003929361,0.00000783098,0.0001265091,0.001225413,0.0002193215,0.0005401288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004466259,"threshold_uncertainty_score":0.008880556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433170529666361,"score_gpt":0.3098361931905487,"score_spread":0.2855044878938851,"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."}}