{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00110873,0.001166571,0.001552147,0.001100866,0.0007300583,0.001841437,0.002371113,0.002262114,0.008382054],"category_scores_gemma":[0.002703324,0.0008124684,0.0008138504,0.001337548,0.000655656,0.002012054,0.00321716,0.001527418,0.004925452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000672597,"about_ca_system_score_gemma":0.001460757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00430522,"about_ca_topic_score_gemma":0.008025353,"domain_scores_codex":[0.9987852,0.000165787,0.0000455252,0.0003670502,0.0004781146,0.0001583875],"domain_scores_gemma":[0.9989175,0.0002596971,0.00008002663,0.0003843605,0.0002879427,0.00007049823],"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":[0.0009001875,0.0002305802,0.001150823,0.0001294374,0.000130173,0.0001719676,0.0001276653,0.03776081,0.1077173,0.00416437,0.01083111,0.8366856],"study_design_scores_gemma":[0.0000395367,0.0001217528,0.001089577,0.00001262747,0.00004046579,0.0002990935,0.0000856825,0.9243317,0.06037045,0.008157936,0.00541715,0.00003401146],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02024608,0.0003164078,0.9730144,0.0001352802,0.0001276595,0.0000615589,0.0002078229,0.00477427,0.00111651],"genre_scores_gemma":[0.1633649,0.0001855211,0.828868,0.0001782373,0.00008727764,0.00008670975,0.0008922436,0.0007085117,0.005628414],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008382054,"threshold_uncertainty_score":0.02804077,"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."}}