{"id":"W1587803272","doi":"10.1007/978-3-540-85990-1_123","title":"Deformable Ultrasound Registration without Reconstruction","year":2008,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Modality (human–computer interaction); Artificial intelligence; Data set; Computer vision; Imaging phantom; Similarity (geometry); Volume (thermodynamics); Set (abstract data type); Task (project management); Adaptability; Pattern recognition (psychology); Image (mathematics); Radiology; Medicine","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":[0.0009747489,0.001019913,0.001294879,0.001127648,0.0005321426,0.001852702,0.0014713,0.002044565,0.015907],"category_scores_gemma":[0.003458756,0.001485086,0.001441681,0.001276365,0.0007611423,0.001558472,0.002866513,0.001913608,0.01274217],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003524489,"about_ca_system_score_gemma":0.00115442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001473235,"about_ca_topic_score_gemma":0.002055275,"domain_scores_codex":[0.9987777,0.000188806,0.00008930726,0.0002934255,0.0005502767,0.0001004984],"domain_scores_gemma":[0.9986477,0.0002075014,0.00008148198,0.0009086189,0.0001224978,0.0000322476],"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":[0.0007350697,0.0002005958,0.001165217,0.0003087006,0.0002011594,0.0005553724,0.0002088984,0.04200441,0.1755037,0.02772861,0.01057078,0.7408174],"study_design_scores_gemma":[0.00009072531,0.0003377354,0.003027946,0.00007166122,0.0001863857,0.005756836,0.0001078541,0.6014171,0.303049,0.01719281,0.0686346,0.0001273324],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005590394,0.000170633,0.9881683,0.0001301415,0.00009880503,0.00007807821,0.0001461842,0.002415038,0.003202547],"genre_scores_gemma":[0.1403337,0.0004677989,0.8285904,0.0003141603,0.0001082921,0.0001596647,0.001102057,0.002278871,0.02664505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.015907,"threshold_uncertainty_score":0.05321425,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0171317479272489,"score_gpt":0.2671213998095198,"score_spread":0.2499896518822709,"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."}}