{"id":"W2795981973","doi":"10.1016/j.neuroimage.2018.04.001","title":"Supervoxel based method for multi-atlas segmentation of brain MR images","year":2018,"lang":"en","type":"article","venue":"NeuroImage","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Voxel; Markov random field; Pairwise comparison; Atlas (anatomy); Inference; Pattern recognition (psychology); Consistency (knowledge bases); Grid; Image segmentation; Grid cell; Computer vision; 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":[0.0007788712,0.0009240082,0.0008770286,0.001900784,0.0006612634,0.001215906,0.001482508,0.001139675,0.005136274],"category_scores_gemma":[0.0009182107,0.0005276655,0.001050165,0.001194874,0.0004523628,0.0007051401,0.0008091165,0.00115397,0.001671999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006639909,"about_ca_system_score_gemma":0.001907105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00820929,"about_ca_topic_score_gemma":0.01632337,"domain_scores_codex":[0.9996056,0.00005102457,0.00002358775,0.0001189189,0.0001456652,0.00005522999],"domain_scores_gemma":[0.9995825,0.0001019193,0.00004142274,0.00005269468,0.0001922544,0.00002926223],"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":[0.0003930992,0.0001362069,0.00103654,0.0003646397,0.0001638703,0.000316328,0.0002432146,0.02280234,0.1688223,0.007929369,0.005471137,0.792321],"study_design_scores_gemma":[0.000056738,0.0002844485,0.003505477,0.00007792129,0.0001843469,0.001316972,0.0001612254,0.7898566,0.1772719,0.008687465,0.01853424,0.00006263809],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009173519,0.0002296812,0.9879258,0.00006653093,0.00002693261,0.00009203826,0.0001322119,0.001572494,0.0007807473],"genre_scores_gemma":[0.06239433,0.000407911,0.9302189,0.0001232201,0.0000518427,0.0001153905,0.0006623353,0.0005193828,0.00550663],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00820929,"threshold_uncertainty_score":0.01718259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05499210990206533,"score_gpt":0.381986541458561,"score_spread":0.3269944315564957,"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."}}