{"id":"W1972154060","doi":"10.1117/12.2043135","title":"Motion and deformation compensation for freehand prostate biopsies","year":2014,"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":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer vision; Computer science; Artificial intelligence; Image registration; Volume (thermodynamics); Motion compensation; Prostate biopsy; Free-form deformation; Prostate; Deformation (meteorology); Medicine; Geology; Image (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005962637,0.0005993128,0.0004615053,0.0007079789,0.0003406021,0.0005652048,0.0009272065,0.0006806341,0.00304572],"category_scores_gemma":[0.001548736,0.0007332904,0.0005633971,0.0004838325,0.0003139219,0.0006583852,0.001026826,0.0005302532,0.001047634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003319884,"about_ca_system_score_gemma":0.0006760056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001783085,"about_ca_topic_score_gemma":0.004366068,"domain_scores_codex":[0.9993429,0.0001264751,0.0000324199,0.0001140826,0.0003439354,0.00004021922],"domain_scores_gemma":[0.9996417,0.0001151774,0.00008040154,0.00009342695,0.00005013668,0.00001911805],"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":[0.0003231973,0.0000435054,0.001353733,0.0002308034,0.0000650791,0.0002212961,0.0001919522,0.04097848,0.4528787,0.00251468,0.001771701,0.4994269],"study_design_scores_gemma":[0.00007017252,0.0006051242,0.0166875,0.00009528829,0.0001046952,0.004001136,0.0001109048,0.4861762,0.4557593,0.005122248,0.03108754,0.0001799254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03810702,0.001288865,0.9565861,0.0001325767,0.00006037895,0.00008230996,0.0001087516,0.002301008,0.001332965],"genre_scores_gemma":[0.2742779,0.001009139,0.717578,0.0001982513,0.00005624217,0.0001204365,0.0004488259,0.0006932786,0.005617914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00304572,"threshold_uncertainty_score":0.01018894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055566295375288,"score_gpt":0.233170523199497,"score_spread":0.2226148602457441,"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."}}