{"id":"W2001800582","doi":"10.1118/1.4888139","title":"SU‐E‐J‐87: Lung Deformable Image Registration Using Surface Mesh Deformation for Dose Distribution Combination","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Hôpital Notre-Dame","funders":"","keywords":"Segmentation; Deformation (meteorology); Image registration; Artificial intelligence; Surface (topology); Lung volumes; Mean squared error; Breathing; Computer science; Computer vision; Mathematics; Geometry; Physics; Lung; Medicine; Image (mathematics); Statistics; Anatomy","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001447525,0.0007208095,0.0006953087,0.001348898,0.0002368282,0.001038145,0.0009159488,0.000812069,0.003511291],"category_scores_gemma":[0.001994267,0.0004206692,0.0009410509,0.0009977872,0.0003165265,0.0006224642,0.0008361585,0.0005874658,0.001351015],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004779493,"about_ca_system_score_gemma":0.0006489169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00135124,"about_ca_topic_score_gemma":0.001752478,"domain_scores_codex":[0.9991814,0.0001473721,0.00004983199,0.0001164497,0.0004642033,0.00004070164],"domain_scores_gemma":[0.9995469,0.0001101882,0.00004969268,0.0001583925,0.0001134593,0.0000213575],"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":[0.001116219,0.000321299,0.01027164,0.0004938046,0.0003604193,0.0002832397,0.0002368184,0.1642751,0.1563926,0.005718981,0.009802293,0.6507275],"study_design_scores_gemma":[0.0001067748,0.0005343428,0.01482192,0.00003563184,0.0001135278,0.001062229,0.00005343813,0.7923476,0.1703576,0.002511433,0.01795311,0.0001023242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07485839,0.000514578,0.9090478,0.0001878331,0.00008866136,0.0002153004,0.0006983913,0.009094929,0.005293963],"genre_scores_gemma":[0.4698811,0.0002986921,0.5184426,0.0001341572,0.00003801538,0.0003180807,0.002893465,0.002963643,0.005030244],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003511291,"threshold_uncertainty_score":0.01174641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01016280381872163,"score_gpt":0.2911193304928927,"score_spread":0.2809565266741711,"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."}}