{"id":"W4390345333","doi":"10.1093/bioinformatics/btad783","title":"ESQmodel: biologically informed evaluation of 2-D cell segmentation quality in multiplexed tissue images","year":2023,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre; University of British Columbia; BC Children's Hospital; BC Cancer Agency","funders":"Canadian Institutes of Health Research; BC Cancer Foundation; Natural Sciences and Engineering Research Council of Canada; Cancer Research UK; Michael Smith Health Research BC","keywords":"Computer science; Segmentation; Quality (philosophy); Computer vision; Biological tissue; Artificial intelligence; Multiplexing; Biomedical engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008856409,0.0001171035,0.0001516701,0.00009899166,0.00003197951,0.00001504346,0.0001338318,0.0001498817,0.00001291701],"category_scores_gemma":[0.0002283263,0.0001038985,0.00006331452,0.0002126313,0.00005402952,0.00001534244,0.00004708283,0.00005401978,0.00002232218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000290133,"about_ca_system_score_gemma":0.0001247249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004697089,"about_ca_topic_score_gemma":0.00007633431,"domain_scores_codex":[0.99881,0.00007193117,0.0005612207,0.0001107758,0.0002638287,0.0001822234],"domain_scores_gemma":[0.999377,0.00003347837,0.0001837698,0.0001987492,0.0001691581,0.00003784284],"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":[0.0000615434,0.00006296956,0.003787702,0.0001377595,0.00001197276,1.640119e-7,0.0005237998,0.002659823,0.9773197,0.00001307234,0.0002840645,0.01513739],"study_design_scores_gemma":[0.002146644,0.0002350004,0.01993083,0.00002117141,0.00002360615,7.392568e-7,0.0009281025,0.03776122,0.9381426,0.00009475663,0.0004948656,0.0002204691],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9946115,0.00005483988,0.002779067,0.00002572561,0.0001167933,0.0004122945,0.00005687554,0.00002532788,0.001917553],"genre_scores_gemma":[0.9885972,0.000180475,0.01015585,0.00006449474,0.00002682662,0.00002736489,0.0008556242,0.000008514041,0.00008367625],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03917714,"threshold_uncertainty_score":0.4236858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06053723972205262,"score_gpt":0.3398534103869885,"score_spread":0.2793161706649359,"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."}}