{"id":"W82086503","doi":"10.1007/978-3-642-23626-6_80","title":"Probabilistic Multi-shape Segmentation of Knee Extensor and Flexor Muscles","year":2011,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Segmentation; Computer science; Artificial intelligence; Ground truth; Probabilistic logic; Pattern recognition (psychology); Image segmentation; Sørensen–Dice coefficient; Task (project management); Computer vision","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":[0.001071537,0.0006667271,0.0007976393,0.00194163,0.000525694,0.001477829,0.001155982,0.002232644,0.001455461],"category_scores_gemma":[0.001799377,0.0009030492,0.001634099,0.001280348,0.0005755869,0.0006846811,0.001083846,0.0007036538,0.0009448233],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004934027,"about_ca_system_score_gemma":0.0009966348,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003680669,"about_ca_topic_score_gemma":0.005912496,"domain_scores_codex":[0.9995599,0.00007981484,0.00002832167,0.000106083,0.0001525115,0.00007322105],"domain_scores_gemma":[0.9992782,0.0002458248,0.00009922167,0.000117124,0.0001941777,0.00006536772],"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":[0.001449859,0.0001738556,0.01266666,0.0005454813,0.0002842574,0.0009416003,0.0004352809,0.2116279,0.3200178,0.003935375,0.002432592,0.4454893],"study_design_scores_gemma":[0.0000391983,0.0001440077,0.01547117,0.00007188209,0.0001119435,0.001598302,0.0001234676,0.9195317,0.05618359,0.004197458,0.002467481,0.00005981474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1829059,0.001607431,0.8111032,0.0003166932,0.00005401102,0.0001080452,0.0003965641,0.001257845,0.002250263],"genre_scores_gemma":[0.6945978,0.0009547236,0.2977032,0.0001807549,0.00007266962,0.00009402038,0.0009459853,0.0005569857,0.004893803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003680669,"threshold_uncertainty_score":0.007318497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02527531373800877,"score_gpt":0.2460737290442458,"score_spread":0.2207984153062371,"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."}}