{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004243801,0.0004976155,0.0003654957,0.0009482313,0.000284432,0.0006635142,0.000489718,0.0004393757,0.001094826],"category_scores_gemma":[0.002051163,0.0002920854,0.0006276825,0.0007999289,0.0002658214,0.0005480322,0.0005354193,0.0005339045,0.0005071239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002708017,"about_ca_system_score_gemma":0.001145261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002213173,"about_ca_topic_score_gemma":0.0032626,"domain_scores_codex":[0.9997248,0.00004919351,0.00002619765,0.00005049899,0.0001168632,0.00003239625],"domain_scores_gemma":[0.9994826,0.0001514137,0.00008814801,0.00009511304,0.0001554941,0.00002718309],"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":[0.0008962697,0.0001162715,0.003946908,0.000292376,0.0001755008,0.0003413292,0.0003314202,0.04730139,0.4079702,0.003665586,0.001383446,0.5335793],"study_design_scores_gemma":[0.00004422662,0.0003746075,0.024436,0.00003722707,0.0003307735,0.001718097,0.0001142494,0.7045631,0.2599391,0.002787301,0.005560274,0.00009494884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1371508,0.000370871,0.8603449,0.0001405032,0.00005268188,0.00006223677,0.00007995743,0.0007473026,0.001050809],"genre_scores_gemma":[0.5740705,0.000556462,0.4221442,0.00004485466,0.00005329997,0.00008238808,0.0003234356,0.0004933489,0.002231518],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002213173,"threshold_uncertainty_score":0.004400611,"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."}}