{"id":"W1972328073","doi":"10.1117/12.2043847","title":"Semi-automatic segmentation of vertebral bodies in volumetric MR images using a statistical shape+pose model","year":2014,"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":15,"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; Segmentation; Computer science; Computer vision; Image segmentation; Active shape model; Pattern recognition (psychology)","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":[0.0008895437,0.0008235347,0.0009277402,0.001566829,0.0004831039,0.001272097,0.001217961,0.001310847,0.000848548],"category_scores_gemma":[0.002182031,0.0009928534,0.001691141,0.0008226963,0.0009328036,0.0009512524,0.000949516,0.0007908658,0.0008878811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005157752,"about_ca_system_score_gemma":0.001284662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003231377,"about_ca_topic_score_gemma":0.007160493,"domain_scores_codex":[0.9991021,0.0002032178,0.0000585269,0.0002091456,0.0003701752,0.00005692629],"domain_scores_gemma":[0.9990464,0.0004133196,0.0001635665,0.0001791689,0.0001567197,0.00004081733],"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":[0.0001750478,0.0001243389,0.003048414,0.0001960923,0.000184734,0.0002813902,0.0002527726,0.416969,0.1721798,0.004948058,0.001760074,0.3998803],"study_design_scores_gemma":[0.00001455588,0.00008354246,0.002239055,0.0000181971,0.00003554514,0.0005478976,0.0000324469,0.9659144,0.02590852,0.003297768,0.001858675,0.00004937473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009009245,0.00008885701,0.9895123,0.00004166492,0.000009059786,0.00004383164,0.00004227817,0.001017298,0.0002355639],"genre_scores_gemma":[0.1853866,0.0003487658,0.8115033,0.0001335235,0.00004415013,0.000175075,0.0005390322,0.0005027942,0.00136672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003231377,"threshold_uncertainty_score":0.006425142,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01171571233543225,"score_gpt":0.2392137364162371,"score_spread":0.2274980240808049,"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."}}