{"id":"W2950569309","doi":"10.1523/eneuro.0012-17.2017","title":"ABLE: An Activity-Based Level Set Segmentation Algorithm for Two-Photon Calcium Imaging Data","year":2017,"lang":"en","type":"article","venue":"eNeuro","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre; Montreal General Hospital","funders":"Biotechnology and Biological Sciences Research Council; Canadian Institutes of Health Research; Engineering and Physical Sciences Research Council; Directorate for Biological Sciences; Natural Sciences and Engineering Research Council of Canada; European Research Council; Government of Canada","keywords":"Active contour model; Segmentation; Calcium imaging; Computer science; Level set (data structures); Pixel; Set (abstract data type); Partition (number theory); Pattern recognition (psychology); Artificial intelligence; Algorithm; Function (biology); Data set; Synthetic data; Image segmentation; Computer vision; Mathematics; Calcium; Biology; Chemistry; Cell biology","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":[0.0009923097,0.0009392094,0.001106271,0.002044458,0.0004865763,0.001418557,0.002471956,0.001915233,0.003328253],"category_scores_gemma":[0.002246323,0.0008369715,0.001566701,0.001165886,0.0005769996,0.000925892,0.001392983,0.001682024,0.001632053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001258068,"about_ca_system_score_gemma":0.001284048,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003870888,"about_ca_topic_score_gemma":0.006219739,"domain_scores_codex":[0.999617,0.00005028076,0.00002957325,0.00009181104,0.0001689817,0.00004232014],"domain_scores_gemma":[0.9995238,0.0002021517,0.00005910809,0.00007872444,0.00009676468,0.00003947087],"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":[0.0002446434,0.0001388053,0.001388255,0.0002560002,0.0002198699,0.0001503515,0.0002015999,0.2283442,0.08197439,0.01247473,0.008151985,0.6664551],"study_design_scores_gemma":[0.00002285185,0.0000221617,0.0003490975,0.00001165338,0.00001386823,0.00006835067,0.00001264992,0.977386,0.01448754,0.00476333,0.002843803,0.00001863645],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004115172,0.0000617148,0.9922016,0.00007227449,0.00001420723,0.00007129111,0.0001576614,0.002977323,0.0003287834],"genre_scores_gemma":[0.04218464,0.00007410224,0.9554335,0.000102589,0.00001545707,0.0002272538,0.0006069931,0.000524187,0.0008312844],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003870888,"threshold_uncertainty_score":0.01113415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07612723795214693,"score_gpt":0.3902291642429426,"score_spread":0.3141019262907956,"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."}}