{"id":"W4402474570","doi":"10.1109/ccece59415.2024.10667291","title":"Expert-guided optimization of ultrasound segmentation models for 3D spine imaging","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University; University of Toronto","funders":"","keywords":"Computer science; Segmentation; Artificial intelligence; 3D ultrasound; SPINE (molecular biology); Computer vision; Image segmentation; Ultrasound; Radiology; Medicine; Bioinformatics","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":[0.002766516,0.001832913,0.0009895755,0.001241956,0.0003836981,0.00157209,0.001191744,0.002072032,0.002625593],"category_scores_gemma":[0.008780109,0.000784383,0.00113061,0.0005861862,0.0007177363,0.0008046348,0.001065456,0.00132735,0.0009001096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00118484,"about_ca_system_score_gemma":0.001734159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008392301,"about_ca_topic_score_gemma":0.01287865,"domain_scores_codex":[0.9990564,0.000357101,0.00006570134,0.0002314863,0.0001998275,0.00008942544],"domain_scores_gemma":[0.9977703,0.001393682,0.0002090035,0.000192934,0.0003387005,0.00009536783],"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":[0.0002839005,0.0001453511,0.002533184,0.0001779958,0.00009562969,0.0001292922,0.000141354,0.8582203,0.009481303,0.001547895,0.003234946,0.1240088],"study_design_scores_gemma":[0.00001352145,0.0000605462,0.0002568738,0.00001492711,0.00001022673,0.00004121359,0.00001720163,0.9960558,0.002381732,0.0007257975,0.0004131679,0.000008981179],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1024031,0.0007930258,0.8885125,0.0005595988,0.0001004331,0.0002565037,0.0003801751,0.004385791,0.002608986],"genre_scores_gemma":[0.6838156,0.0003202817,0.3107806,0.0004568098,0.00004874487,0.000275147,0.0008782781,0.0006666792,0.002757898],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008392301,"threshold_uncertainty_score":0.01668692,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01768628421021705,"score_gpt":0.2791673482825784,"score_spread":0.2614810640723614,"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."}}