{"id":"W2789928581","doi":"10.1109/access.2018.2807698","title":"Glioma Segmentation Using a Novel Unified Algorithm in Multimodal MRI Images","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Hausdorff distance; Computer science; Segmentation; Robustness (evolution); Artificial intelligence; Sørensen–Dice coefficient; Dice; Image segmentation; Cluster analysis; Euclidean distance; Pattern recognition (psychology); Algorithm; Mathematics; 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":[0.001295422,0.001273325,0.001732879,0.003615209,0.0008714765,0.001773783,0.001818571,0.002436573,0.001705095],"category_scores_gemma":[0.002398117,0.0007780964,0.002116094,0.002733382,0.0007023187,0.001942175,0.001830803,0.001160162,0.0009926396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008955474,"about_ca_system_score_gemma":0.001654732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004919925,"about_ca_topic_score_gemma":0.005710562,"domain_scores_codex":[0.9985113,0.0001991975,0.0001551926,0.0004546478,0.0005347681,0.0001448683],"domain_scores_gemma":[0.9993661,0.0001273708,0.0001031706,0.000111252,0.0002486609,0.00004344839],"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":[0.0002216307,0.00009449976,0.001955971,0.0002802703,0.0002228396,0.0002582619,0.0002846002,0.09537992,0.08063844,0.008582494,0.003619768,0.8084614],"study_design_scores_gemma":[0.00003097563,0.0001375861,0.001410285,0.00002557764,0.00009685903,0.0005324141,0.00005111112,0.9640316,0.02489542,0.00414441,0.004583999,0.00005966384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004380775,0.0002343934,0.9941856,0.00004566861,0.00001816078,0.00004693643,0.00002421236,0.0007686779,0.0002956146],"genre_scores_gemma":[0.05446397,0.0002938519,0.9435003,0.00007741195,0.00005268715,0.0001716513,0.0001766423,0.0001801295,0.001083294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004919925,"threshold_uncertainty_score":0.009782553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0470482957096771,"score_gpt":0.3730901712265821,"score_spread":0.3260418755169049,"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."}}