{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008347661,0.0008594691,0.001066537,0.0008646993,0.0003436577,0.00114637,0.001501029,0.000833193,0.001318892],"category_scores_gemma":[0.001753327,0.0007077141,0.00130442,0.0009517614,0.000607838,0.0009402533,0.002189793,0.001289882,0.0009274592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004907652,"about_ca_system_score_gemma":0.001371574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002865424,"about_ca_topic_score_gemma":0.003158801,"domain_scores_codex":[0.9989629,0.0002389812,0.00004884251,0.0001897521,0.0004972988,0.00006229828],"domain_scores_gemma":[0.9995304,0.0001411384,0.00009484775,0.0001121427,0.00008681013,0.00003462972],"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":[0.0002952361,0.0001669976,0.0009398753,0.0003486468,0.0001849588,0.0003325312,0.0002026203,0.5638053,0.07886064,0.01594878,0.003888993,0.3350253],"study_design_scores_gemma":[0.00001019085,0.00004680092,0.0002291511,0.000005071112,0.00001222427,0.0001890774,0.000008845375,0.9867078,0.009121492,0.001602947,0.002046665,0.00001969948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003960735,0.00008400564,0.9950597,0.00005196288,0.000009673468,0.00002257474,0.00002812337,0.0003328383,0.0004504726],"genre_scores_gemma":[0.1260597,0.0002445561,0.8704357,0.0001002236,0.00004171316,0.0001452,0.0003020452,0.0004341117,0.002236638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002865424,"threshold_uncertainty_score":0.005697489,"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."}}