{"id":"W4380997279","doi":"10.3389/fncir.2023.952921","title":"mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops","year":2023,"lang":"en","type":"article","venue":"Frontiers in Neural Circuits","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lunenfeld-Tanenbaum Research Institute; University of Toronto; Mount Sinai Hospital","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; Canadian Institutes of Health Research; National Institutes of Health","keywords":"Connectomics; Computer science; Segmentation; Artificial intelligence; Deep learning; Annotation; Leverage (statistics); Preprocessor; Graphical user interface; Software; Ground truth; Visualization; Machine learning; Pattern recognition (psychology); Connectome; Neuroscience; Operating system","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":[0.001081571,0.002055225,0.0009089763,0.001520567,0.0003629764,0.0016159,0.003240809,0.0009698088,0.0897184],"category_scores_gemma":[0.005627151,0.001016741,0.001183552,0.0009339961,0.0005996633,0.001959569,0.003292554,0.001800511,0.03628451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000906958,"about_ca_system_score_gemma":0.001152064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001547935,"about_ca_topic_score_gemma":0.002752588,"domain_scores_codex":[0.9994389,0.00006929443,0.00005137257,0.0001576359,0.0002040705,0.00007863733],"domain_scores_gemma":[0.9985211,0.0007919517,0.0001101086,0.0002060673,0.0002587875,0.0001119938],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001607932,0.0001585466,0.003745637,0.002251509,0.0003622676,0.00140198,0.0009121514,0.02350539,0.02965264,0.02132319,0.6277095,0.2873693],"study_design_scores_gemma":[0.0008199695,0.0002210812,0.005382563,0.0006503173,0.0001208994,0.001613477,0.0002949745,0.3015773,0.1123263,0.06599086,0.5106243,0.0003780079],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.003257265,0.0002430022,0.4050924,0.000239418,0.00011225,0.0001142331,0.01290612,0.5729696,0.005065773],"genre_scores_gemma":[0.1219951,0.00105338,0.5314635,0.001617308,0.0001334744,0.002747531,0.05491612,0.2569428,0.02913077],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.0897184,"threshold_uncertainty_score":0.3001379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01393093942109519,"score_gpt":0.3226495656570101,"score_spread":0.308718626235915,"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."}}