{"id":"W2802443974","doi":"10.3791/57440","title":"Automated Slide Scanning and Segmentation in Fluorescently-labeled Tissues Using a Widefield High-content Analysis System","year":2018,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Innovates - Health Solutions; Alberta Cancer Foundation","keywords":"Workflow; Computer science; Segmentation; Software; Protocol (science); Personalization; Artificial intelligence; Computer hardware; Computer vision; Pattern recognition (psychology); Pathology; Operating system; Database; Medicine","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":[0.002026458,0.0009310676,0.0006447346,0.003485491,0.00140431,0.00149224,0.00140125,0.0011388,0.009542204],"category_scores_gemma":[0.001573188,0.001050959,0.0007693179,0.001251031,0.0007198361,0.001404887,0.001021954,0.001452015,0.005466477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008868175,"about_ca_system_score_gemma":0.001555572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001622294,"about_ca_topic_score_gemma":0.004424362,"domain_scores_codex":[0.9985196,0.0001704302,0.0001586135,0.0004073916,0.0005948951,0.0001490198],"domain_scores_gemma":[0.998484,0.0003713261,0.0001477228,0.0004362671,0.0004757085,0.00008499841],"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":[0.0001116769,0.00007467339,0.0009261302,0.0002391566,0.00003298965,0.0001837765,0.0001464254,0.0007448623,0.9577572,0.002092264,0.00267333,0.03501758],"study_design_scores_gemma":[0.0000384315,0.000169447,0.008243158,0.00008654859,0.00006882931,0.001170447,0.0001157224,0.02582778,0.927416,0.001494973,0.03526588,0.0001026197],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06571465,0.000754355,0.9109249,0.0003321971,0.0002271363,0.001035997,0.001181314,0.01196568,0.007863716],"genre_scores_gemma":[0.04083725,0.0006278278,0.9490679,0.0001198207,0.00004216068,0.001068591,0.00134428,0.001257805,0.005634382],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009542204,"threshold_uncertainty_score":0.0319218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03060190812736149,"score_gpt":0.4136303379623452,"score_spread":0.3830284298349837,"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."}}