{"id":"W6903070560","doi":"10.1016/j.ejrai.2025.100034","title":"Improving medical image segmentation with SAM2: analyzing the impact of object characteristics and finetuning on multi-planar datasets.","year":2025,"lang":"en","type":"article","venue":"European Journal of Radiology Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Segmentation; Dice; Sørensen–Dice coefficient; Object (grammar); Intersection (aeronautics); Image segmentation; Pattern recognition (psychology); Object detection; Scale-space segmentation","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":[0.004885993,0.001881394,0.001248584,0.002663967,0.0006756784,0.002627503,0.001436881,0.002027811,0.001660317],"category_scores_gemma":[0.01031789,0.0007176696,0.00144527,0.001901976,0.0005175844,0.001573584,0.001355467,0.001024114,0.001305719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008994494,"about_ca_system_score_gemma":0.001095244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004651192,"about_ca_topic_score_gemma":0.009847043,"domain_scores_codex":[0.998515,0.0003655652,0.0001229337,0.0004101695,0.000455304,0.0001310949],"domain_scores_gemma":[0.9971096,0.001598627,0.0003126095,0.0004061128,0.0004616725,0.0001114652],"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":[0.002177684,0.0003944377,0.03842372,0.001863562,0.00160403,0.0006702787,0.0006524324,0.2253627,0.1231166,0.002714438,0.01734989,0.5856703],"study_design_scores_gemma":[0.00005968001,0.0006465116,0.01407359,0.00009502031,0.0002485988,0.001192388,0.0002094667,0.9026973,0.06697445,0.002533193,0.01117439,0.00009547211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3515346,0.01131576,0.5924768,0.00142331,0.000512507,0.0005013248,0.004533355,0.03243655,0.005265759],"genre_scores_gemma":[0.5976573,0.002049019,0.3825273,0.0008406928,0.0001646528,0.0002814781,0.01119865,0.002384232,0.002896682],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004885993,"threshold_uncertainty_score":0.02583987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03075355265644513,"score_gpt":0.3236239499293269,"score_spread":0.2928703972728818,"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."}}