{"id":"W2109213053","doi":"10.5555/602099.602104","title":"Direct surface extraction from 3D freehand ultrasound images","year":2002,"lang":"en","type":"article","venue":"cIRcle (University of British Columbia)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Voxel; Artificial intelligence; Computer science; Pixel; Computer vision; 3D ultrasound; Noise (video); Data set; Sampling (signal processing); Ultrasound; Image resolution; Feature extraction; Surface (topology); Pattern recognition (psychology); Image (mathematics); Mathematics; Acoustics; Geometry","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":[0.0001466375,0.00004265501,0.0001819507,0.00002925197,0.0001835999,0.000244782,0.0006158962,0.0000819118,0.00091084],"category_scores_gemma":[0.00006300091,0.0001503242,0.00007148115,0.0002752912,0.0002026673,0.001194466,0.0001238733,0.0001227931,0.00007406343],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000605165,"about_ca_system_score_gemma":0.00001753566,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07836387,"about_ca_topic_score_gemma":0.02245611,"domain_scores_codex":[0.9988496,0.0000862583,0.0001180871,0.0004115477,0.000348208,0.0001863146],"domain_scores_gemma":[0.9990598,0.0001834865,0.0001294626,0.0003791058,0.0001195678,0.0001285787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000001259525,0.0001570066,0.00271997,0.00001487377,0.00002831137,0.0001791707,0.0002815736,0.000004466371,0.03109681,4.49463e-7,0.04266685,0.9228492],"study_design_scores_gemma":[0.0007702147,0.00008483212,0.9942539,0.0001140901,0.00002940942,0.00005989432,0.0002652008,0.001687178,0.001552925,0.0002341249,0.0006329553,0.0003153108],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4301112,0.0003356381,0.5624393,0.0001050018,0.0001916744,0.00013168,0.0001105933,0.0003989587,0.0061759],"genre_scores_gemma":[0.8705679,0.0004397363,0.1265389,0.00007276143,0.00002432261,2.773276e-7,0.00001214625,0.000007620737,0.002336337],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9915339,"threshold_uncertainty_score":0.9973059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01070017222066957,"score_gpt":0.1951282265233755,"score_spread":0.1844280543027059,"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."}}