{"id":"W3012075782","doi":"10.1002/mp.14127","title":"Creation of an anthropomorphic CT head phantom for verification of image segmentation","year":2020,"lang":"en","type":"article","venue":"Medical Physics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; Genentech; National Institutes of Health; University of Liverpool; National Institute on Aging; National Institute for Health and Care Research; Northern California Institute for Research and Education; Alzheimer's Disease Neuroimaging Initiative; GE Healthcare; Pfizer; Biogen; BioClinica; Roche; University of Southern California; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Merck; Alzheimer's Drug Discovery Foundation; Takeda Pharmaceutical Company; AbbVie; Fujirebio Europe; Alzheimer's Association","keywords":"Imaging phantom; Segmentation; Hounsfield scale; Computer science; Artificial intelligence; Voxel; Contouring; Image segmentation; Biomedical engineering; Computer vision; Nuclear medicine; Medicine; Computed tomography; Radiology; Computer graphics (images)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001378475,0.0008355348,0.0003921214,0.001005929,0.0003253493,0.0007957284,0.0008929775,0.0009504555,0.006054884],"category_scores_gemma":[0.003242337,0.0006200676,0.0005005131,0.0006726951,0.0007246603,0.0004081102,0.000805791,0.0006612272,0.001466224],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004785077,"about_ca_system_score_gemma":0.0008781035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036014,"about_ca_topic_score_gemma":0.001225133,"domain_scores_codex":[0.9995073,0.0001150582,0.00005602408,0.00009136653,0.0001878026,0.00004247188],"domain_scores_gemma":[0.9980102,0.000761095,0.0001564576,0.000604159,0.0003910487,0.00007705892],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001619983,0.000865849,0.005632159,0.001053281,0.0001498723,0.002910271,0.00132722,0.05799685,0.8327537,0.004507718,0.00346358,0.08771942],"study_design_scores_gemma":[0.0003416053,0.002690643,0.01855608,0.0002155466,0.0003113405,0.010897,0.0003611289,0.1062422,0.8249704,0.00197109,0.03321684,0.0002260413],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3144265,0.0006849588,0.6687834,0.000544776,0.0002746958,0.001902945,0.002326122,0.003811067,0.007245492],"genre_scores_gemma":[0.5781563,0.0006545884,0.4115386,0.0003450994,0.00004001884,0.001490054,0.00221894,0.0009472261,0.00460914],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006054884,"threshold_uncertainty_score":0.02025557,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04015493706732023,"score_gpt":0.360070164925745,"score_spread":0.3199152278584247,"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."}}