{"id":"W2023667179","doi":"10.1117/12.2081542","title":"Deep learning for automatic localization, identification, and segmentation of vertebral bodies in volumetric MR images","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Artificial intelligence; Thresholding; Segmentation; Computer science; Computer vision; Vertebra; Image segmentation; Voxel; Pattern recognition (psychology); Deep learning; Image (mathematics); Anatomy; 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":[0.000908911,0.0009486407,0.0007577296,0.001342201,0.0004131729,0.0007874873,0.0014233,0.001167156,0.001198345],"category_scores_gemma":[0.002028102,0.0007790857,0.0009112556,0.00112922,0.0006023097,0.0009160566,0.001252625,0.001221768,0.0006843354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162407,"about_ca_system_score_gemma":0.001339945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009833932,"about_ca_topic_score_gemma":0.01525066,"domain_scores_codex":[0.9994824,0.00009946214,0.0000307246,0.0001242652,0.0001864421,0.00007660592],"domain_scores_gemma":[0.9994782,0.0001974143,0.00009113429,0.00008324107,0.000124293,0.00002575832],"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":[0.0001473847,0.00011971,0.001575653,0.0001761475,0.00010441,0.0001233658,0.0001099242,0.2875321,0.04903269,0.006168136,0.004521014,0.6503894],"study_design_scores_gemma":[0.000006520904,0.00002309367,0.0004685334,0.00001073884,0.00001086043,0.00003610475,0.000009523468,0.9892426,0.006692962,0.002770911,0.0007203388,0.000007762339],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01722671,0.0003945348,0.9794347,0.0001363272,0.00001976972,0.00003751376,0.0001087384,0.002037605,0.0006041229],"genre_scores_gemma":[0.3067273,0.0004946537,0.6890652,0.0002033696,0.00004201606,0.0001391196,0.0006803124,0.0002879937,0.002360156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009833932,"threshold_uncertainty_score":0.01955336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012975156175053,"score_gpt":0.232642526660607,"score_spread":0.2225127750988564,"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."}}