{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003299855,0.0001603781,0.0002925605,0.000557016,0.00002173744,0.0001253192,0.0008180967,0.00006482005,0.00007457012],"category_scores_gemma":[0.0001347011,0.0001532353,0.00007179403,0.0009572111,0.0001546152,0.0003974272,0.000354673,0.0001523926,0.0001754598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001376021,"about_ca_system_score_gemma":0.00007493493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005690618,"about_ca_topic_score_gemma":0.0000098261,"domain_scores_codex":[0.9986452,0.00006246765,0.0003210302,0.0004138287,0.000166079,0.0003914092],"domain_scores_gemma":[0.9989242,0.0001143433,0.00006121756,0.0006221693,0.00007808966,0.0001999995],"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":[0.00000835083,0.0001938044,0.01787088,0.00003136951,0.00001169427,0.00007209033,0.0001726371,0.00002117285,0.9727102,0.001004706,0.001754434,0.006148682],"study_design_scores_gemma":[0.0005473811,0.00008391788,0.0229363,0.00001891983,0.000007758731,0.0000297507,0.00002103655,0.0001482996,0.9716737,0.0006353707,0.003685496,0.0002120921],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9489302,0.00002380358,0.04746875,0.0005654558,0.0005358802,0.0002187538,0.00005246691,0.00009851751,0.002106186],"genre_scores_gemma":[0.8149632,3.581721e-7,0.1842276,0.0004644186,0.0000242519,0.00001498698,0.000004846397,0.00001040843,0.0002900211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1367588,"threshold_uncertainty_score":0.6248756,"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."}}