{"id":"W4402620540","doi":"10.1007/s11548-024-03249-1","title":"Robust unsupervised texture segmentation for motion analysis in ultrasound images","year":2024,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal; Université de Montréal; École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer vision; Artificial intelligence; Computer science; Modality (human–computer interaction); Segmentation; Ultrasound; Texture (cosmology); Motion analysis; Radiology; Medicine; Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003166343,0.000555188,0.0008473731,0.001807785,0.000358098,0.0007670796,0.0007189901,0.0006261014,0.001405744],"category_scores_gemma":[0.001324493,0.0004512238,0.0009245812,0.001132865,0.0004407094,0.0004456744,0.0005668503,0.0007145491,0.0006182766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004399233,"about_ca_system_score_gemma":0.0008877834,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004317835,"about_ca_topic_score_gemma":0.007013233,"domain_scores_codex":[0.9997053,0.00004777682,0.00001864693,0.00006768633,0.0001105412,0.00005005941],"domain_scores_gemma":[0.9995083,0.0001835203,0.00008281086,0.00007184599,0.0001254962,0.00002815088],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004773625,0.0001216576,0.001357269,0.0002077966,0.0001118325,0.0001067941,0.0001045855,0.06377379,0.371627,0.002340888,0.002174332,0.5575967],"study_design_scores_gemma":[0.00002514008,0.00009433273,0.004679766,0.00001774882,0.00005957297,0.0001875333,0.00004131464,0.9259663,0.06434978,0.002300706,0.002252575,0.00002534494],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03383891,0.0002885167,0.9640213,0.00007289218,0.00003207385,0.00004411481,0.0001603176,0.001115031,0.0004268777],"genre_scores_gemma":[0.3091053,0.0005250871,0.6849744,0.00008819727,0.00009760826,0.0001378657,0.0009764248,0.0007636726,0.003331446],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004317835,"threshold_uncertainty_score":0.008585453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02471124808420227,"score_gpt":0.2935403640400008,"score_spread":0.2688291159557985,"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."}}