{"id":"W2081345262","doi":"10.1007/s11548-014-1098-5","title":"Brain-shift compensation by non-rigid registration of intra-operative ultrasound images with preoperative MR images based on residual complexity","year":2014,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Imaging phantom; Image registration; Computer science; Artificial intelligence; Residual; Computer vision; Feature (linguistics); Wavelet; Data set; Fiducial marker; Similarity measure; Image-guided surgery; Pattern recognition (psychology); Medicine; Image (mathematics); Radiology; Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001420644,0.0002024623,0.0004729563,0.0003482096,0.00009582758,0.0001646984,0.0005002145,0.00009799268,0.00002210651],"category_scores_gemma":[0.0003268896,0.0001542346,0.00009135513,0.0001364021,0.0005510495,0.0006667696,0.00004278205,0.0003110679,0.000001071884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006350948,"about_ca_system_score_gemma":0.0001865226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001645344,"about_ca_topic_score_gemma":0.000003916112,"domain_scores_codex":[0.9973373,0.0008030853,0.0008028443,0.0002929318,0.0006035262,0.0001602683],"domain_scores_gemma":[0.9945762,0.00349782,0.0008660257,0.0001980948,0.000752164,0.0001097247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.004066686,0.003067548,0.1164237,0.0002236115,0.002727382,0.0006538405,0.006347219,0.006358495,0.127502,0.01797136,0.542493,0.1721652],"study_design_scores_gemma":[0.004712971,0.003992596,0.6645767,0.0009567566,0.0000758826,0.002012436,0.0001106628,0.05141662,0.2676897,0.003000048,0.0005582579,0.0008973246],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0663185,0.00002887731,0.9265274,0.00635455,0.0003955333,0.0001220167,0.0000174262,0.00003028347,0.0002053963],"genre_scores_gemma":[0.8774284,0.00002351124,0.1198955,0.002354448,0.0002098446,0.00000500168,0.00005773491,0.000008025473,0.00001748672],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8111099,"threshold_uncertainty_score":0.6289504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01635744386011855,"score_gpt":0.2843601649858402,"score_spread":0.2680027211257216,"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."}}