{"id":"W2142507690","doi":"10.1109/ccece.2007.51","title":"Statistical Deformation Model For Intensity Based Image Registration","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Affine transformation; Principal component analysis; Wavelet; Subspace topology; Artificial intelligence; Deformation (meteorology); Image registration; Computer science; Computer vision; Transformation (genetics); Pattern recognition (psychology); Wavelet transform; Image (mathematics); Mathematics; Algorithm; Geometry","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":[0.001611076,0.0007970968,0.00113322,0.00158155,0.0003817549,0.001243665,0.002046807,0.00176091,0.002655973],"category_scores_gemma":[0.003894499,0.0006702867,0.001261352,0.001753104,0.001650338,0.002054786,0.0009852364,0.001696449,0.001994501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001068269,"about_ca_system_score_gemma":0.001160432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002565838,"about_ca_topic_score_gemma":0.001721421,"domain_scores_codex":[0.9987761,0.0002617585,0.00005572157,0.0002470415,0.0005837231,0.00007559949],"domain_scores_gemma":[0.9989429,0.0003846186,0.000183224,0.000196249,0.0002502374,0.00004277222],"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":[0.00005838274,0.00004831438,0.000470126,0.00008469634,0.00006465074,0.0001685937,0.00009618868,0.6641189,0.01183714,0.2513221,0.002626725,0.0691041],"study_design_scores_gemma":[0.000003888057,0.0000207656,0.0001742511,0.000004365445,0.000007295796,0.00006815399,0.000006039839,0.964395,0.0009316403,0.03244571,0.001926049,0.00001674352],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001579372,0.0001278703,0.9971187,0.0001429655,0.00002995252,0.0000149635,0.00003110936,0.0001626911,0.0007923617],"genre_scores_gemma":[0.4654538,0.002540468,0.5013272,0.0005661405,0.0005306244,0.0007975489,0.0009234654,0.0008509849,0.02700975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002655973,"threshold_uncertainty_score":0.008885145,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137369457745342,"score_gpt":0.3273444424187352,"score_spread":0.2959707478412818,"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."}}