{"id":"W4394744135","doi":"10.1109/access.2024.3388293","title":"Two-Step Rigid and Non-Rigid Image Registration for the Alignment of Three-Dimensional Echocardiography Sequences From Multiple Views","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Artificial intelligence; Computer vision; Image registration; Hausdorff distance; Computer science; Mutual information; Image fusion; Image quality; Real-time MRI; Speckle noise; Speckle pattern; Pattern recognition (psychology); Magnetic resonance imaging; Image (mathematics); Radiology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005303828,0.0001328897,0.0001745362,0.00009575419,0.0001052204,0.0004509312,0.0007643516,0.00004253806,0.00001330464],"category_scores_gemma":[0.00003578543,0.00008797547,0.0001117806,0.000305351,0.0001909729,0.001026607,0.000121934,0.00009097144,0.000003483002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000204923,"about_ca_system_score_gemma":0.00006089017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008539191,"about_ca_topic_score_gemma":0.0001286163,"domain_scores_codex":[0.9986392,0.00004929791,0.000338694,0.0004014998,0.0004201173,0.0001511364],"domain_scores_gemma":[0.9986408,0.0006718236,0.0001249905,0.0004116225,0.00008852904,0.00006222907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008848846,0.0002244301,0.008027336,0.0005480672,0.0007164772,0.00008124777,0.001387491,0.0004067377,0.3955986,0.003988889,0.1165934,0.4723388],"study_design_scores_gemma":[0.0006632651,0.0001417954,0.004754445,0.0002858885,0.00008279797,0.000007064906,0.00003398098,0.3246731,0.6494986,0.01804346,0.001512627,0.0003030078],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01354832,0.0007236367,0.9834933,0.0006757858,0.0005712539,0.0006764999,0.00004743181,0.0001283119,0.0001354385],"genre_scores_gemma":[0.7310806,0.0001134999,0.2679019,0.0004740085,0.0001519767,0.0002318736,0.00001608393,0.00001072606,0.0000193461],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7175323,"threshold_uncertainty_score":0.434834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04405789638816045,"score_gpt":0.3440203945864601,"score_spread":0.2999624981982996,"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."}}