{"id":"W2087829477","doi":"10.1016/j.media.2009.10.007","title":"An automatic geometrical and statistical method to detect acoustic shadows in intraoperative ultrasound brain images","year":2009,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Segmentation; Ultrasound; Acoustic shadow; 3D ultrasound; Statistical model; Acoustic impedance; Pattern recognition (psychology); Acoustics; Ultrasonic sensor","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003200074,0.0002895895,0.0007461843,0.001591269,0.000105425,0.0004774829,0.001038917,0.0001638601,0.001132204],"category_scores_gemma":[0.0095477,0.000242295,0.0001018322,0.005414157,0.0002188274,0.0007967598,0.0001582804,0.0005103684,0.00003690704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001191668,"about_ca_system_score_gemma":0.0001277888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000171955,"about_ca_topic_score_gemma":0.00008705555,"domain_scores_codex":[0.9952099,0.001075138,0.0007895018,0.0009387358,0.001416678,0.0005700157],"domain_scores_gemma":[0.9951492,0.002791013,0.00009510334,0.000630321,0.0001432679,0.001191103],"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":[0.000007824106,0.0002638455,0.0003427594,0.0000170713,0.0001354946,0.0006226535,0.0008266038,0.0000633435,0.0297574,0.0002511103,0.002724135,0.9649878],"study_design_scores_gemma":[0.0009459074,0.001123434,0.07839705,0.0000594493,0.000423027,0.0001072967,0.0002273169,0.8853024,0.02710176,0.005491506,0.00003831301,0.000782504],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004329406,0.00005699908,0.9903603,0.004519586,0.00002118905,0.0002928167,0.00001340031,0.0002976575,0.0001086873],"genre_scores_gemma":[0.2421656,0.0000205976,0.7506686,0.007015337,0.00003795958,0.00003489571,0.00002102886,0.000009022951,0.00002704839],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9642053,"threshold_uncertainty_score":0.9997809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00835886815253391,"score_gpt":0.3524337114892521,"score_spread":0.3440748433367182,"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."}}