{"id":"W2649385687","doi":"10.1155/2017/5703216","title":"Segmentation Method for Magnetic Resonance-Guided High-Intensity Focused Ultrasound Therapy Planning","year":2017,"lang":"en","type":"article","venue":"Journal of Healthcare Engineering","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"Consejo Nacional de Ciencia y Tecnología","keywords":"Segmentation; Magnetic resonance imaging; Computer science; Image segmentation; High-intensity focused ultrasound; Ultrasound; Ablation; Artificial intelligence; Radiation treatment planning; Computer vision; Radiology; Medical physics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004271454,0.0005504838,0.0004921807,0.001144669,0.0005907462,0.0009386118,0.0009052283,0.000847819,0.003327875],"category_scores_gemma":[0.00108695,0.0004167833,0.0007600467,0.001148288,0.000432866,0.0006911014,0.0005540407,0.000492802,0.001266526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006609647,"about_ca_system_score_gemma":0.001734726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005059496,"about_ca_topic_score_gemma":0.004098491,"domain_scores_codex":[0.9994754,0.0000996042,0.00004359797,0.0001206943,0.0002149205,0.00004577018],"domain_scores_gemma":[0.9995622,0.0001284503,0.0000492253,0.00005546512,0.0001796634,0.00002495492],"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":[0.0003499409,0.00009066069,0.001361717,0.0004199367,0.00009635698,0.0003766334,0.0003838673,0.2250185,0.1605049,0.0153169,0.005837301,0.5902433],"study_design_scores_gemma":[0.00003003477,0.0000924129,0.001399098,0.00003469618,0.00005030722,0.0004133469,0.00004897356,0.9257814,0.05023159,0.004862386,0.0170073,0.00004850781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004712514,0.000234059,0.9931065,0.00005268195,0.00002824081,0.00005425534,0.00004757865,0.0007100806,0.001054026],"genre_scores_gemma":[0.1434017,0.0004959127,0.8527555,0.00005936195,0.00004789696,0.0001622226,0.0003815721,0.0003471496,0.002348655],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005059496,"threshold_uncertainty_score":0.01113284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03663076118118069,"score_gpt":0.3172633830879298,"score_spread":0.2806326219067491,"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."}}