{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004699099,0.00136462,0.001038908,0.002135918,0.0007118864,0.002653064,0.001957257,0.002091336,0.005542153],"category_scores_gemma":[0.01195272,0.0006008879,0.001488235,0.001037141,0.0009859288,0.001552452,0.002033716,0.0009858448,0.001845743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001815338,"about_ca_system_score_gemma":0.001488351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004766458,"about_ca_topic_score_gemma":0.007337434,"domain_scores_codex":[0.9987094,0.0002967525,0.00007576809,0.0003260174,0.0005043425,0.00008770431],"domain_scores_gemma":[0.9963905,0.002213585,0.0003768915,0.0003668955,0.0004976135,0.0001544862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001408867,0.0003068004,0.02691452,0.001481283,0.0007267859,0.0003878226,0.0006286406,0.658537,0.07504813,0.01435849,0.0285357,0.191666],"study_design_scores_gemma":[0.0000364886,0.0000992245,0.002824747,0.00005027442,0.00002448227,0.0001258313,0.00005441344,0.9718766,0.01534603,0.006894154,0.002621083,0.00004670671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08561441,0.0009278337,0.8686157,0.0006282445,0.0001198014,0.0002977851,0.007188746,0.03336691,0.003240432],"genre_scores_gemma":[0.3987096,0.0004463143,0.5779971,0.0006296705,0.0000742997,0.0005315161,0.01277412,0.005766511,0.003070833],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005542153,"threshold_uncertainty_score":0.0248515,"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."}}