{"id":"W1978756306","doi":"10.1016/j.optlaseng.2010.06.011","title":"Medical image registration using stochastic optimization","year":2010,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convexity; Image registration; Divergence (linguistics); Tsallis entropy; Artificial intelligence; Entropy (arrow of time); Degenerate energy levels; Thresholding; Image (mathematics); Gaussian; Kullback–Leibler divergence; Computer vision; Algorithm; Pattern recognition (psychology); Physics","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":[0.002019842,0.0007185997,0.001853355,0.001330023,0.0005922426,0.001142955,0.001114375,0.001636742,0.001380612],"category_scores_gemma":[0.005090422,0.001353352,0.001847354,0.001159099,0.001280295,0.001107038,0.002013655,0.001436166,0.000583559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001327611,"about_ca_system_score_gemma":0.002061752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590525,"about_ca_topic_score_gemma":0.004052996,"domain_scores_codex":[0.9989451,0.0003429941,0.00006840779,0.0001953934,0.0003954081,0.00005289785],"domain_scores_gemma":[0.9984939,0.0008744796,0.0002128688,0.000150569,0.0002051979,0.00006294731],"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":[0.0001746244,0.00005940229,0.0004964898,0.0001357052,0.0001516708,0.00009406148,0.0000895202,0.8593326,0.01070522,0.03829564,0.001571234,0.08889383],"study_design_scores_gemma":[0.000008620611,0.00001451367,0.00007093884,0.000004445143,0.000009307726,0.00003459546,0.000002706214,0.9908861,0.001347931,0.007163303,0.000449737,0.000007772989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001812082,0.00004639658,0.9976665,0.00007168221,0.000009469648,0.00001684293,0.00001043256,0.000165734,0.0002008786],"genre_scores_gemma":[0.2118665,0.0002960927,0.7834449,0.0001748202,0.00009209867,0.0003176589,0.0001834583,0.0004332343,0.003191289],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003590525,"threshold_uncertainty_score":0.01068205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006380674554696346,"score_gpt":0.2469321932776152,"score_spread":0.2405515187229188,"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."}}