{"id":"W2952735543","doi":"10.1109/tmi.2019.2905770","title":"Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":301,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of British Columbia; Montreal Neurological Institute and Hospital; Toronto Metropolitan University; University of Calgary; McGill University","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Allergy and Infectious Diseases; National Institutes of Health; Hotchkiss Brain Institute, University of Calgary; Universitair Medisch Centrum Utrecht; Institute for Basic Science; Daegu Gyeongbuk Institute of Science and Technology; University College London Hospitals NHS Foundation Trust; Télécom Paris; Multiple Sclerosis Society; Ministry of Advanced Education; Schweizerische Multiple Sklerose Gesellschaft; Ministerio de Ciencia y Tecnología; University of British Columbia; Huazhong University of Science and Technology; Natural Sciences and Engineering Research Council of Canada; Ministerio de Economía y Competitividad; Leids Universitair Medisch Centrum; National Natural Science Foundation of China; National Research Foundation of Korea; Ministerio de Educación, Cultura y Deporte; Sun Yat-sen University; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; ZonMw; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; European Regional Development Fund; King's College London; National Research Foundation; Alzheimer Society; National Institute for Health and Care Research; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Technische Universität München; Canadian Institutes of Health Research; Alzheimer's Society; Inselspital, Universitätsspital Bern; Hotchkiss Brain Institute; Skolkovo Institute of Science and Technology; Ministry of Advanced Education and Skills Development; Brigham and Women's Hospital; National University Health System; Nvidia; Ministry of Education; University of Bern; Université Paris-Saclay; Universiteit Utrecht; Universität Basel; University of Dundee; McGill University; Universitat Politècnica de Catalunya; Sungkyunkwan University; Universitat de Girona; University College London; Vrije Universiteit Amsterdam; National Science Foundation","keywords":"Segmentation; Hyperintensity; Artificial intelligence; Robustness (evolution); Scanner; Computer science; Fluid-attenuated inversion recovery; Percentile; Hausdorff distance; Pattern recognition (psychology); Image segmentation; Sørensen–Dice coefficient; Computer vision; Mathematics; Magnetic resonance imaging; Statistics; Medicine; Radiology","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":[0.02336288,0.005573375,0.003162906,0.006618885,0.002059539,0.004899458,0.003520405,0.005298398,0.00495501],"category_scores_gemma":[0.0470194,0.0009914658,0.003869334,0.002187502,0.002029958,0.002911049,0.00594043,0.002547598,0.006648232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00186581,"about_ca_system_score_gemma":0.00275506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005963864,"about_ca_topic_score_gemma":0.007710376,"domain_scores_codex":[0.9751584,0.007610495,0.002691159,0.006593584,0.006332521,0.001613939],"domain_scores_gemma":[0.9599704,0.01140881,0.002789515,0.008014872,0.01476707,0.003049342],"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":[0.008606001,0.003385376,0.0378232,0.008759907,0.004552075,0.00147452,0.001955519,0.04771242,0.06708492,0.003072822,0.2985435,0.5170298],"study_design_scores_gemma":[0.003348186,0.01182736,0.2105504,0.002524164,0.002894527,0.007812877,0.004058706,0.3621947,0.181486,0.01264121,0.1989791,0.001682801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6646167,0.02627683,0.1528654,0.003964321,0.009062097,0.007614712,0.06261793,0.0449711,0.02801088],"genre_scores_gemma":[0.578059,0.002585263,0.1994068,0.001541579,0.00155712,0.003821045,0.1862459,0.008259005,0.01852449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02336288,"threshold_uncertainty_score":0.1235562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01084822036847822,"score_gpt":0.2927377472235015,"score_spread":0.2818895268550233,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). 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