{"id":"W2806788401","doi":"10.1016/j.media.2018.05.010","title":"A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy","year":2018,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Artificial intelligence; Segmentation; Sørensen–Dice coefficient; Prostate biopsy; Deep learning; Convolutional neural network; Hausdorff distance; Ultrasound; Pattern recognition (psychology); Computer vision; Image segmentation; Prostate; Radiology; Medicine; Cancer","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":[0.0005501431,0.000492505,0.0005398258,0.0005925357,0.0003344243,0.0008129989,0.001151342,0.001359847,0.00270823],"category_scores_gemma":[0.001038301,0.0005811119,0.0006142603,0.0004776899,0.0002759979,0.0006779385,0.001172482,0.0009112665,0.0006384769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006229191,"about_ca_system_score_gemma":0.001154351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007625503,"about_ca_topic_score_gemma":0.01379516,"domain_scores_codex":[0.9997467,0.00004191319,0.00001427483,0.00005432491,0.00009743316,0.00004522087],"domain_scores_gemma":[0.9996797,0.0001176149,0.00002681707,0.00003779,0.0001103757,0.00002778743],"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":[0.0003570786,0.0001629464,0.001250381,0.0001289729,0.00009060103,0.0001736771,0.00008153194,0.2515374,0.04847579,0.003876516,0.00398925,0.6898759],"study_design_scores_gemma":[0.00000410659,0.00002401058,0.0002431077,0.000005340945,0.000009116534,0.00005633746,0.0000053641,0.993614,0.004732692,0.0007535605,0.0005466497,0.000005823075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02061187,0.0004206839,0.9763657,0.0001943539,0.00005047437,0.00004285747,0.00008960487,0.001134839,0.001089494],"genre_scores_gemma":[0.4608147,0.0005599121,0.5281489,0.0003804877,0.00008801637,0.0001046203,0.0003676711,0.0003162265,0.009219415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007625503,"threshold_uncertainty_score":0.01516223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290786442534562,"score_gpt":0.3004009372686144,"score_spread":0.2874930728432688,"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."}}