{"id":"W2317689312","doi":"10.1117/12.2217079","title":"3D prostate MR-TRUS non-rigid registration using dual optimization with volume-preserving constraint","year":2016,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Constraint (computer-aided design); Volume (thermodynamics); Dual (grammatical number); Computer science; Image registration; Computer vision; Artificial intelligence; Mathematics; Geometry; Physics; Image (mathematics)","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":[0.0007937109,0.0003115617,0.0003401108,0.0001315241,0.0001174829,0.0002663101,0.001157392,0.000156722,0.00002218189],"category_scores_gemma":[0.0005933293,0.0002161224,0.0002438364,0.0004224519,0.0003865093,0.001974332,0.0002694908,0.0002113883,0.000001148034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002395718,"about_ca_system_score_gemma":0.0001046065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000152419,"about_ca_topic_score_gemma":1.603767e-7,"domain_scores_codex":[0.9972872,4.50676e-8,0.0007602474,0.0005252954,0.00101199,0.0004152246],"domain_scores_gemma":[0.9968489,0.0001254476,0.0006496861,0.0001145158,0.002099445,0.0001619845],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007754899,0.0001156938,0.0004219005,0.0003435083,0.0002475396,5.919796e-7,0.0003562771,0.001345789,0.8726944,0.1193361,0.001941831,0.003118815],"study_design_scores_gemma":[0.001569629,0.0005423864,0.0002954415,0.0009234169,0.00009135434,0.00006491745,0.0004588802,0.6377884,0.3566539,0.000856557,0.0002567512,0.0004984092],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7248211,0.00001402353,0.2710754,0.002308698,0.0001300881,0.0006615564,0.00001988594,0.0001748874,0.0007944056],"genre_scores_gemma":[0.09896451,0.00004448785,0.9003218,0.0001122621,0.0001828557,0.0001161166,0.000005035115,0.00004145552,0.0002115063],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6364425,"threshold_uncertainty_score":0.8813217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226681631192103,"score_gpt":0.2392000477555768,"score_spread":0.2269332314436557,"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."}}