{"id":"W4412464586","doi":"10.1038/s41598-025-09211-8","title":"Energy-based segmentation methods for images with non-Gaussian noise","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Okanagan University College; University of British Columbia, Okanagan Campus","funders":"Natural Sciences and Engineering Research Council of Canada; National Aeronautics and Space Administration","keywords":"Segmentation; Computer science; Noise (video); Energy (signal processing); Artificial intelligence; Gaussian; Gaussian noise; Pattern recognition (psychology); Computer vision; Image (mathematics); Statistics; Mathematics; Chemistry; Computational chemistry","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":[0.001053744,0.0007635407,0.001051779,0.002167712,0.000551413,0.001208655,0.001525197,0.001583974,0.001406121],"category_scores_gemma":[0.002958015,0.0005636866,0.000939161,0.001305409,0.001077431,0.001817041,0.0009677616,0.001073854,0.0009085676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000824426,"about_ca_system_score_gemma":0.0007193605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393187,"about_ca_topic_score_gemma":0.002118909,"domain_scores_codex":[0.9992297,0.0001414459,0.00005188163,0.0001890181,0.0003392807,0.00004863942],"domain_scores_gemma":[0.9991139,0.0004028664,0.0001237722,0.0001304901,0.0002007551,0.00002823184],"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":[0.0002079523,0.0001020763,0.001266881,0.0004061027,0.0001319095,0.0002883833,0.0004686967,0.2321623,0.09982927,0.0573373,0.00234493,0.6054542],"study_design_scores_gemma":[0.000007192059,0.00003009912,0.0005692237,0.00002165537,0.00001871869,0.0001942968,0.00003152292,0.962131,0.01695872,0.01678484,0.00322546,0.00002726448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002554645,0.0001835525,0.9966549,0.00004639301,0.0000150114,0.00001487915,0.00000884475,0.0001886109,0.0003332981],"genre_scores_gemma":[0.1054261,0.0004877196,0.8914325,0.0001116388,0.00006328234,0.00009395471,0.0001241371,0.0002990597,0.001961707],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002167712,"threshold_uncertainty_score":0.005981624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01208265466156309,"score_gpt":0.3449839336562043,"score_spread":0.3329012789946412,"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."}}