{"id":"W2099633258","doi":"10.1109/biorob.2008.4762877","title":"Real-time parametric curved needle segmentation in 3D ultrasound images","year":2008,"lang":"en","type":"article","venue":"","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"CUDA; Computer science; Graphics processing unit; Computer vision; Artificial intelligence; Ultrasound; 3D ultrasound; Brachytherapy; Segmentation; Biomedical engineering; Radiology; Engineering; Medicine; Radiation therapy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003896044,0.0000654695,0.00007340519,0.0001094326,0.00003162156,0.00001345092,0.00005169232,0.00002986815,0.0001826317],"category_scores_gemma":[0.00001783292,0.00006675105,0.00001740202,0.0004689905,0.00001413412,0.00006928595,0.000005054996,0.0000480923,0.000246144],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003933041,"about_ca_system_score_gemma":0.000005761737,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001401615,"about_ca_topic_score_gemma":0.00000596607,"domain_scores_codex":[0.9996063,0.000005622901,0.0001240331,0.00008273567,0.00006500602,0.0001163793],"domain_scores_gemma":[0.9997122,0.00012079,0.00001004551,0.0001131625,0.00001249433,0.0000313123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00000324307,0.0001721801,0.06997639,0.00003258658,0.000037449,0.000009145838,0.0006071929,0.3998021,0.5045977,0.000554399,0.02197045,0.002237138],"study_design_scores_gemma":[0.001127501,0.00003983316,0.745502,0.00001554253,0.00002123155,0.00004548305,0.0002524533,0.1399987,0.1110488,0.0006024724,0.0006616788,0.0006843518],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9578249,0.00004746695,0.01310344,0.00002603514,0.00004109764,0.0001728653,0.000004873902,0.0003181222,0.0284612],"genre_scores_gemma":[0.9734154,0.000318719,0.02532879,0.00001138155,0.00002299585,0.00003108923,0.00002918055,0.00001567477,0.0008267749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6755257,"threshold_uncertainty_score":0.3163765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01345543076416287,"score_gpt":0.2285038360796416,"score_spread":0.2150484053154787,"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."}}