{"id":"W2043931347","doi":"10.1016/j.bpj.2013.11.3562","title":"Identifying Active Neurons from In Vivo 2-Photon Calcium Imaging of the Brain via Pixel Correlation Analysis and Region-Growing Segmentation","year":2014,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Montreal Neurological Institute and Hospital; McGill University","funders":"","keywords":"Calcium imaging; Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Region of interest; Calcium; Pixel; Computer vision; Active contour model; In vivo; Correlation; Image segmentation; Biological system; Biology; Chemistry; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008620949,0.00008792827,0.000127153,0.00006967422,0.00008494647,0.00002319076,0.0001073715,0.00004571793,0.00000200005],"category_scores_gemma":[0.00005966427,0.00007205302,0.00009608211,0.000193286,0.0001044241,0.00002639514,0.00007236037,0.0001532872,1.683598e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002706718,"about_ca_system_score_gemma":0.00001261314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000691236,"about_ca_topic_score_gemma":0.00001405264,"domain_scores_codex":[0.9992962,0.0001215335,0.0001852291,0.0001794413,0.0001057371,0.0001118684],"domain_scores_gemma":[0.9995733,0.00003216663,0.0001837115,0.0001344618,0.0000414849,0.00003482052],"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":[0.00003263434,0.00002077055,0.01933217,0.000002143584,0.00002149414,7.980878e-7,0.0001032751,0.0000880142,0.9780144,0.000008918969,0.00002231729,0.002353075],"study_design_scores_gemma":[0.0001885679,0.00002621924,0.03869238,0.00002528768,0.00005867268,0.000006860962,0.0000930852,0.00619562,0.9543176,0.0002999862,0.00002314061,0.00007261358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5482566,0.000009893745,0.4515558,0.00008518653,0.00003708599,0.000042878,0.000002993432,0.000002601367,0.000006886585],"genre_scores_gemma":[0.9960861,0.0000150434,0.003636116,0.0001421348,0.00008965143,0.000002818935,0.000008815838,0.000008912079,0.00001042314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4479197,"threshold_uncertainty_score":0.2938237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00843263013025026,"score_gpt":0.2804449118248959,"score_spread":0.2720122816946456,"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."}}