{"id":"W2067678991","doi":"10.1109/iembs.2010.5627187","title":"Real time MRI prostate segmentation based on wavelet multiscale products flow tracking","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Manitoba; CancerCare Manitoba","funders":"","keywords":"Prostate cancer; Computer science; Wavelet; Segmentation; Prostate; Artificial intelligence; Magnetic resonance imaging; Data set; Image segmentation; Computer vision; Process (computing); Wavelet transform; Tracking (education); Noise (video); Pattern recognition (psychology); Radiology; Medicine; Cancer; 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":[0.0006456102,0.0003516191,0.0004597259,0.001060803,0.000199833,0.0006488563,0.0004189065,0.0005772521,0.0007635811],"category_scores_gemma":[0.001126634,0.0003252562,0.0004984506,0.000797762,0.0003033917,0.0004954977,0.0003483328,0.0003639599,0.0003252042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000321036,"about_ca_system_score_gemma":0.0005336412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002127555,"about_ca_topic_score_gemma":0.002348508,"domain_scores_codex":[0.9997711,0.00004203526,0.0000136935,0.00006282474,0.00008987051,0.00002048862],"domain_scores_gemma":[0.9997154,0.000109196,0.0000490612,0.00003934741,0.00006946679,0.00001741611],"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":[0.0003460904,0.00008688869,0.001722886,0.000129184,0.00005703312,0.0001478362,0.0001448535,0.1317658,0.2034715,0.00417233,0.00152948,0.6564261],"study_design_scores_gemma":[0.00001190318,0.0000517621,0.001842921,0.00000794571,0.00001694517,0.000160066,0.000008149852,0.9723064,0.02310404,0.0009037227,0.001564547,0.00002165769],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02995302,0.0002407251,0.9683101,0.00006476248,0.00002898745,0.00004296489,0.00005692704,0.0007815047,0.0005210403],"genre_scores_gemma":[0.2731463,0.0004936639,0.7240381,0.0000546544,0.00004524152,0.00005744455,0.0002219962,0.0001371181,0.001805482],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002127555,"threshold_uncertainty_score":0.004230261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01002790205059666,"score_gpt":0.2660971620080737,"score_spread":0.256069259957477,"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."}}