{"id":"W1985161468","doi":"10.1016/j.media.2011.05.010","title":"An integrated approach to segmentation and nonrigid registration for application in image-guided pelvic radiotherapy","year":2011,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":64,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health","keywords":"Segmentation; Computer science; Artificial intelligence; Computer vision; Image registration; Image segmentation; Radiation treatment planning; Radiation therapy; Medicine; Image (mathematics); Radiology","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":[0.001067182,0.001007854,0.001387583,0.001661966,0.0007342243,0.001767227,0.002198484,0.001445752,0.003553075],"category_scores_gemma":[0.002012978,0.001218908,0.001760399,0.002062498,0.0004846859,0.001434286,0.001926813,0.001236643,0.001532573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006324044,"about_ca_system_score_gemma":0.001637283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004975565,"about_ca_topic_score_gemma":0.01018805,"domain_scores_codex":[0.9989958,0.0001254839,0.00006319416,0.0001669691,0.0005882544,0.00006013719],"domain_scores_gemma":[0.9994289,0.0001540702,0.00005230904,0.0001088006,0.0002302204,0.00002565248],"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":[0.0001980046,0.000253645,0.0005508065,0.0001849634,0.0002401584,0.000100065,0.0001773092,0.0910411,0.09939405,0.007286111,0.003664927,0.7969089],"study_design_scores_gemma":[0.00002231972,0.00008638536,0.001189378,0.00001561087,0.0000841402,0.0002141072,0.000026544,0.9574975,0.03034978,0.004680031,0.005788718,0.00004540729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001832188,0.0000918366,0.9965369,0.00003197583,0.00001804153,0.00004188726,0.0000285096,0.001091717,0.0003270083],"genre_scores_gemma":[0.02869427,0.0001802534,0.9685996,0.00005658007,0.00002952732,0.0001081085,0.0001449524,0.000528713,0.001658052],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004975565,"threshold_uncertainty_score":0.01188618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01600170604017961,"score_gpt":0.3277133562666227,"score_spread":0.3117116502264431,"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."}}