{"id":"W2077593871","doi":"10.1118/1.3471020","title":"Deformable image registration of heterogeneous human lung incorporating the bronchial tree","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Institutes of Health; Terry Fox Foundation; National Cancer Institute; Cancer Care Ontario","keywords":"Hyperelastic material; Image registration; Elasticity (physics); Finite element method; Human lung; Lung; Voxel; Tree (set theory); Mathematics; Medicine; Biomedical engineering; Radiology; Materials science; Image (mathematics); Physics; Computer science; Mathematical analysis; Artificial intelligence; Composite material","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":[0.001054445,0.0003940345,0.000400431,0.000645361,0.0001389278,0.0006255023,0.0004996439,0.000822696,0.0008332955],"category_scores_gemma":[0.003975223,0.0003464874,0.0004968413,0.0003304147,0.0003836117,0.0005747213,0.0004542875,0.0003656238,0.0004441183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000323152,"about_ca_system_score_gemma":0.0003293947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001628301,"about_ca_topic_score_gemma":0.001825151,"domain_scores_codex":[0.9995106,0.0001547765,0.00003083561,0.0001141579,0.0001675461,0.00002203991],"domain_scores_gemma":[0.9991217,0.0004421128,0.0001249666,0.0002103666,0.00007762844,0.00002315095],"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":[0.0005997421,0.0001224966,0.0160053,0.0003373913,0.0001969706,0.000502213,0.0004013916,0.4986309,0.3337982,0.0009963729,0.0004726327,0.1479362],"study_design_scores_gemma":[0.00001905695,0.0002727298,0.01781773,0.00003080252,0.00007259024,0.0009122269,0.0001065672,0.8933966,0.08453324,0.0007365132,0.002053873,0.00004814734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4858034,0.0006306455,0.5105906,0.0001696497,0.00003440959,0.00009735618,0.0001878727,0.001187875,0.001298115],"genre_scores_gemma":[0.9139795,0.0002513617,0.08435494,0.00005895548,0.000009631066,0.00004638912,0.0002852134,0.0001449235,0.0008691307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001628301,"threshold_uncertainty_score":0.005576551,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01212819451559783,"score_gpt":0.2880815464810549,"score_spread":0.2759533519654571,"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."}}