{"id":"W196093984","doi":"10.1007/978-3-319-10443-0_14","title":"Optimized PatchMatch for Near Real Time and Accurate Label Fusion","year":2014,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Dice; Artificial intelligence; Computer science; Pattern recognition (psychology); Fusion; Sørensen–Dice coefficient; Computation; Image segmentation; Computer vision; Mathematics; Algorithm; Statistics","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":[0.001393823,0.0001617152,0.0002186679,0.0001498384,0.0002487002,0.000557329,0.001163809,0.00007310831,0.000007282566],"category_scores_gemma":[0.000343261,0.0001336475,0.00002678449,0.0007653081,0.0003826369,0.0007065408,0.000609102,0.0001383857,0.000009740656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004574275,"about_ca_system_score_gemma":0.0001077834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004770736,"about_ca_topic_score_gemma":0.000003446619,"domain_scores_codex":[0.998143,0.000090691,0.0002667904,0.0006895649,0.0004118234,0.000398135],"domain_scores_gemma":[0.9983774,0.0007186832,0.0001025062,0.0005093977,0.0001362408,0.0001557443],"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":[0.00001017605,0.00003458056,0.00005439358,0.00001422608,0.000001490776,0.000002561859,0.0006273559,0.002536462,0.0201684,0.0001667935,0.00005985292,0.9763237],"study_design_scores_gemma":[0.0006044268,0.000218114,0.0002933923,0.00003596782,0.000001531128,0.000008792337,1.309103e-7,0.9488019,0.04194609,0.007905726,0.00002213769,0.0001617613],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01288031,0.00001470394,0.9848982,0.001304598,0.0002773706,0.0003560346,0.000001196103,0.0002474962,0.00002006028],"genre_scores_gemma":[0.07199199,0.00001325938,0.9260988,0.001787379,0.00007581883,0.00002122657,0.000002095673,0.000007085746,0.000002318389],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.976162,"threshold_uncertainty_score":0.5449987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01422548147577159,"score_gpt":0.2902908348874141,"score_spread":0.2760653534116425,"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."}}