{"id":"W2510908842","doi":"10.3389/fninf.2016.00037","title":"AxonSeg: Open Source Software for Axon and Myelin Segmentation and Morphometric Analysis","year":2016,"lang":"en","type":"article","venue":"Frontiers in Neuroinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Heart Institute; Université Laval; Institut Universitaire en Santé Mentale de Québec; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Axon; Segmentation; Artificial intelligence; Computer science; Myelin; Pattern recognition (psychology); Image segmentation; Graphical user interface; Software; Image processing; Computer vision; Anatomy; Neuroscience; Biology; Image (mathematics); Central nervous 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002632592,0.0001240856,0.0002286339,0.0004300049,0.0000558415,0.00008839864,0.000187983,0.00007764297,0.000002354633],"category_scores_gemma":[0.0003645473,0.0001003349,0.00005738678,0.0005105741,0.00006535074,0.00003628583,0.0002318302,0.00003730714,4.580103e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000167994,"about_ca_system_score_gemma":0.00002004019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009628969,"about_ca_topic_score_gemma":0.00001549482,"domain_scores_codex":[0.9992132,0.00002963785,0.0003003596,0.0002069442,0.00008595199,0.0001639016],"domain_scores_gemma":[0.999431,0.00003891331,0.0001386695,0.0002726066,0.00006143587,0.00005743378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002725606,0.00009017247,0.4153652,0.0001640636,0.0007706023,0.000003404827,0.0003463883,0.0001928454,0.04238486,0.00001084371,0.06630002,0.4740991],"study_design_scores_gemma":[0.01293351,0.002464429,0.08836292,0.0001927634,0.003712569,0.00006033392,0.003083743,0.07526495,0.5568295,0.001289092,0.2528283,0.002977825],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2538946,0.0003677779,0.7451459,0.00006688773,0.00002096834,0.0003949768,0.0000165868,0.00001483119,0.00007745726],"genre_scores_gemma":[0.4407233,0.005875764,0.5504225,0.0008471282,0.00005556793,0.0001562369,0.0002796336,0.00004855219,0.001591263],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5144446,"threshold_uncertainty_score":0.4091538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008734298136995113,"score_gpt":0.2542633862635632,"score_spread":0.2455290881265681,"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."}}