{"id":"W2088099880","doi":"10.1117/12.2043092","title":"A statistical shape+pose model for segmentation of wrist CT images","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kingston General Hospital; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Principal component analysis; Wrist; Computer vision; Active shape model; Kinematics; Population; Carpal bones; Pattern recognition (psychology); Image segmentation; Statistical model; Medicine; 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":[0.0008655767,0.0005695381,0.0006248712,0.0009999474,0.0002597643,0.0007916522,0.001123809,0.001108052,0.001011293],"category_scores_gemma":[0.00243412,0.0006262342,0.001121163,0.001117721,0.0009141276,0.0007286085,0.0005310656,0.0007709122,0.0007983129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007705058,"about_ca_system_score_gemma":0.001039711,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005433124,"about_ca_topic_score_gemma":0.006285748,"domain_scores_codex":[0.9994662,0.0001348244,0.00003633876,0.0001483509,0.0001781666,0.00003611403],"domain_scores_gemma":[0.9995764,0.0001659086,0.00009440126,0.0000690684,0.00007304573,0.00002119786],"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":[0.00008496108,0.00002871814,0.001092648,0.00004520851,0.00003846842,0.0001401943,0.00007131328,0.9004843,0.01524314,0.01905669,0.0008719958,0.06284237],"study_design_scores_gemma":[0.000002450484,0.00001526964,0.0003792125,0.000002413565,0.000004980993,0.0000667275,0.000004080559,0.993421,0.000723291,0.004844849,0.0005277085,0.000008052631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006063494,0.00007724208,0.9931408,0.00008254558,0.00001287316,0.00001852339,0.00008328602,0.000216357,0.0003049511],"genre_scores_gemma":[0.5043805,0.0008713673,0.4845187,0.0002419138,0.0001730222,0.0005145764,0.001197613,0.0005240064,0.007578289],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005433124,"threshold_uncertainty_score":0.01080298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0229227634902717,"score_gpt":0.2718072084268748,"score_spread":0.2488844449366031,"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."}}