{"id":"W4391876934","doi":"10.1117/12.3008783","title":"3D U-Net with region of interest segmentation of kidneys and masses in computed tomography scans","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Robarts Clinical Trials; University of Alberta","funders":"","keywords":"Computed tomography; Segmentation; Image segmentation; Computer science; Artificial intelligence; Radiology; Medicine","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":[0.0008902073,0.0008635293,0.0007283894,0.001331964,0.0004512665,0.001289346,0.001212823,0.001771585,0.001827338],"category_scores_gemma":[0.001807513,0.0007443739,0.001274156,0.0007734863,0.0006701697,0.0006930395,0.0008112754,0.0006420148,0.0005837571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001674741,"about_ca_system_score_gemma":0.001115416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01939817,"about_ca_topic_score_gemma":0.02601596,"domain_scores_codex":[0.99971,0.00006056761,0.00002344435,0.0001033815,0.00006932736,0.00003317236],"domain_scores_gemma":[0.9996029,0.0001831894,0.00004780228,0.00004517725,0.00009449726,0.00002656617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002314936,0.00005440884,0.002430015,0.00005422314,0.00004669871,0.0001700796,0.00008022736,0.9094492,0.006103106,0.001083364,0.001350913,0.07894628],"study_design_scores_gemma":[0.000001869555,0.000009944193,0.0001434867,0.000003708563,0.000003032544,0.00002496806,0.00000310419,0.9980275,0.0012614,0.000320277,0.0001970349,0.000003662513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1087826,0.0005468333,0.8802933,0.0003523158,0.0000886475,0.0002656788,0.0007346572,0.006888679,0.00204723],"genre_scores_gemma":[0.5826629,0.0003359457,0.4107139,0.0003002581,0.00004146046,0.0002457488,0.001181855,0.000439438,0.00407832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01939817,"threshold_uncertainty_score":0.03857052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02826956236914296,"score_gpt":0.2806294429983758,"score_spread":0.2523598806292328,"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."}}