{"id":"W2041150517","doi":"10.1002/cyto.a.20951","title":"Constrained watershed method to infer morphology of mammalian cells in microscopic images","year":2010,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; Watershed; High resolution; Computer vision; Pattern recognition (psychology); Frame (networking); Mathematical morphology; Biological system; Image segmentation; Image (mathematics); Image processing; Biology; Remote sensing; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0005519071,0.0004367506,0.0005252836,0.001065078,0.0002475159,0.0005675807,0.0008318,0.0007049536,0.001085566],"category_scores_gemma":[0.001593244,0.0003936462,0.0005471981,0.0008207881,0.0004749464,0.0007567652,0.0004946846,0.0007572955,0.0003772045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004371235,"about_ca_system_score_gemma":0.0009394976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002855711,"about_ca_topic_score_gemma":0.004169541,"domain_scores_codex":[0.9997475,0.00004820503,0.00001406801,0.0000539317,0.0001191344,0.00001712607],"domain_scores_gemma":[0.999702,0.0001593234,0.00003517774,0.00003246027,0.00005895615,0.00001204362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001333009,0.00008967485,0.001135039,0.0002756824,0.000114455,0.0003385857,0.0002785041,0.1750307,0.3232878,0.01685765,0.002602854,0.4798558],"study_design_scores_gemma":[0.00001917951,0.00003259522,0.0009695521,0.000009167252,0.00001706092,0.0002237204,0.00002834234,0.944236,0.0447276,0.005780148,0.003937219,0.00001942855],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007454434,0.00007741588,0.9917569,0.0000331322,0.000007467387,0.00003214608,0.00003182691,0.0003366515,0.0002700691],"genre_scores_gemma":[0.08225716,0.0003098512,0.9156722,0.00003406857,0.0000164395,0.0001282461,0.00020025,0.0001151466,0.001266547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002855711,"threshold_uncertainty_score":0.005678177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01163957836606931,"score_gpt":0.290030926387743,"score_spread":0.2783913480216737,"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."}}