{"id":"W2131230393","doi":"10.1007/s11548-006-0001-4","title":"3D Segmentation of Medical Images Using a Fast Multistage Hybrid Algorithm","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"Robarts Clinical Trials","funders":"National Natural Science Foundation of China","keywords":"Computer science; Robustness (evolution); Segmentation; Fast marching method; Computer vision; Algorithm; Artificial intelligence; Dilation (metric space); Image segmentation; Pattern recognition (psychology); Mathematics","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":[0.0009395423,0.0008427648,0.000918215,0.00199365,0.0006021498,0.001236698,0.001270857,0.001586849,0.003118723],"category_scores_gemma":[0.001558301,0.001197895,0.001387191,0.001338833,0.0004599314,0.001001571,0.001165302,0.0009052809,0.0009487012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005629266,"about_ca_system_score_gemma":0.001309344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003907922,"about_ca_topic_score_gemma":0.007170948,"domain_scores_codex":[0.99939,0.00009032369,0.00004918186,0.00009650863,0.0003207161,0.00005324947],"domain_scores_gemma":[0.9989335,0.0005207444,0.0000770195,0.0001467669,0.0002820639,0.0000398242],"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":[0.0005090193,0.0001346621,0.001532408,0.0004759079,0.0002650735,0.000275512,0.0003386051,0.1587886,0.3020697,0.007958913,0.002264775,0.5253868],"study_design_scores_gemma":[0.00002128339,0.00008459672,0.001043433,0.00002145471,0.00005687664,0.0004137345,0.00002468881,0.9530162,0.03961757,0.002269142,0.003387065,0.00004385623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006897156,0.0001257811,0.9918295,0.00003420336,0.00001524343,0.00004598073,0.000030703,0.0005515506,0.0004699078],"genre_scores_gemma":[0.04359752,0.0001404667,0.9544312,0.00003293465,0.00001330777,0.00008931043,0.0001005534,0.0001371433,0.001457541],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003907922,"threshold_uncertainty_score":0.0104332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01641674329643051,"score_gpt":0.2992375017257806,"score_spread":0.2828207584293501,"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."}}