{"id":"W2023717954","doi":"10.1007/s10439-010-0198-9","title":"Identifying Same-Cell Contours in Image Stacks: A Key Step in Making 3D Reconstructions","year":2010,"lang":"en","type":"article","venue":"Annals of Biomedical Engineering","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Institute of Molecular and Cell Biology; Natural Sciences and Engineering Research Council of Canada; Max-Planck-Gesellschaft; Deutsche Forschungsgemeinschaft","keywords":"Centroid; Stack (abstract data type); Image (mathematics); Artificial intelligence; Computer vision; Set (abstract data type); Confocal; Isotropy; Pattern recognition (psychology); Computer science; Plane (geometry); Algorithm; Key (lock); Data set; Mathematics; Geometry; Optics; Physics","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":[0.002732046,0.001705562,0.001442017,0.002925643,0.001861731,0.006016111,0.002219854,0.002538498,0.0044931],"category_scores_gemma":[0.00927648,0.002556673,0.001125108,0.002024094,0.001353676,0.004752337,0.002531717,0.006477692,0.003328348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007821788,"about_ca_system_score_gemma":0.002913677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002394283,"about_ca_topic_score_gemma":0.003979682,"domain_scores_codex":[0.9992501,0.0001233754,0.00007766848,0.0001272524,0.000338176,0.00008334129],"domain_scores_gemma":[0.9956866,0.001753016,0.0004058119,0.001052574,0.0009361685,0.0001657683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003071429,0.0001549894,0.003115555,0.001057413,0.0001547476,0.0006676791,0.001523472,0.02063365,0.6434228,0.02158855,0.005411583,0.3019624],"study_design_scores_gemma":[0.00005084633,0.0001189711,0.01020099,0.0002813734,0.0002779392,0.001928427,0.001211951,0.2338941,0.6443759,0.05886084,0.04840726,0.0003914296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01300678,0.0002151324,0.9826578,0.0002125457,0.00004847077,0.0002006366,0.0002258868,0.002456993,0.0009757379],"genre_scores_gemma":[0.02099588,0.0004642621,0.9765463,0.00005474929,0.00001949385,0.0001216187,0.0002511047,0.001213176,0.0003334697],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006016111,"threshold_uncertainty_score":0.01503086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0122288015234771,"score_gpt":0.2967016734406192,"score_spread":0.284472871917142,"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."}}