{"id":"W4408188883","doi":"10.1016/j.bspc.2025.107747","title":"Enhanced retinal arteries and veins segmentation through deep learning with conditional random fields","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"Precursory Research for Embryonic Science and Technology; Japan Science and Technology Agency; Japan Science and Technology Corporation","keywords":"Segmentation; Retinal; Artificial intelligence; Computer science; Conditional random field; Deep learning; Pattern recognition (psychology); Medicine; Ophthalmology","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":[0.000633949,0.0006678133,0.0007222741,0.0009766737,0.0002419245,0.0009040457,0.0008113532,0.001035661,0.00167183],"category_scores_gemma":[0.001254455,0.0005054228,0.0007921099,0.0005919258,0.0003252417,0.0006725887,0.0008687799,0.001189747,0.0005865688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005901387,"about_ca_system_score_gemma":0.001163724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00661706,"about_ca_topic_score_gemma":0.009657279,"domain_scores_codex":[0.99978,0.00003803018,0.0000102802,0.00006089726,0.00006803124,0.00004272261],"domain_scores_gemma":[0.9996017,0.0001635846,0.00004992641,0.00005675382,0.00009389258,0.00003422143],"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":[0.0005754455,0.0002156549,0.002516743,0.000187863,0.0001644406,0.0002168541,0.0001020287,0.3682442,0.05310744,0.01070177,0.007779934,0.5561877],"study_design_scores_gemma":[0.00000675766,0.00001543656,0.0002540099,0.000007191961,0.00001246108,0.00004336944,0.000003279139,0.9921611,0.005080823,0.001909827,0.000499402,0.000006272103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02430896,0.0003708889,0.9723564,0.000256549,0.00004813526,0.00002663626,0.0001659239,0.001438564,0.001028081],"genre_scores_gemma":[0.5021052,0.0005428553,0.4900635,0.0003505584,0.0001094155,0.00006746054,0.0007207047,0.0003809313,0.00565944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00661706,"threshold_uncertainty_score":0.01315713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004827450009120422,"score_gpt":0.2600727030189702,"score_spread":0.2552452530098497,"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."}}