{"id":"W2006642938","doi":"10.1016/j.ydbio.2011.05.656","title":"3D volumetric ex-vivo mouse embryo imaging and image registration using MRI, Micro-CT and Optical Projection Tomography","year":2011,"lang":"en","type":"article","venue":"Developmental Biology","topic":"Anatomy and Medical Technology","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Ex vivo; Biology; Projection (relational algebra); Preclinical imaging; Tomography; Image registration; Embryo; Biomedical engineering; In vivo; Computer vision; Image (mathematics); Optics; Cell biology; Computer science; Medicine; Physics","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":[0.0008343427,0.0006034577,0.000514438,0.001113162,0.0002813739,0.001208594,0.0006441745,0.0006597341,0.001911447],"category_scores_gemma":[0.0008989092,0.0008239336,0.000554518,0.0008198285,0.0004532133,0.0007992823,0.0006446658,0.0007999532,0.0005249735],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003725619,"about_ca_system_score_gemma":0.000956926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001389003,"about_ca_topic_score_gemma":0.002487488,"domain_scores_codex":[0.999643,0.00005686294,0.00004138315,0.00006399392,0.0001720281,0.00002268311],"domain_scores_gemma":[0.9995471,0.0000913165,0.0001306845,0.0001172042,0.00008794545,0.0000257921],"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":[0.000316963,0.00006964598,0.003427244,0.0002462096,0.00006209833,0.0003570392,0.0001504464,0.01038679,0.9450732,0.003463884,0.0006928712,0.03575361],"study_design_scores_gemma":[0.00004066652,0.0002364728,0.01793486,0.00005156412,0.0001154331,0.003924518,0.0001295313,0.05761282,0.9098299,0.001319868,0.008699675,0.0001047493],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1433138,0.0009463746,0.8478052,0.0002029067,0.00006396963,0.0002422175,0.001360179,0.001901702,0.004163625],"genre_scores_gemma":[0.4163045,0.001589714,0.5754696,0.00009900725,0.00002627016,0.0003670081,0.001161556,0.0007892112,0.004193055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001911447,"threshold_uncertainty_score":0.006394446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157277152376584,"score_gpt":0.2159142717448251,"score_spread":0.2043415002210593,"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."}}