{"id":"W2056236664","doi":"10.1109/tmi.2013.2294630","title":"Nonrigid Registration of Ultrasound and MRI Using Contextual Conditioned Mutual Information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mutual information; Image registration; Artificial intelligence; Computer science; Similarity (geometry); Stochastic gradient descent; Metric (unit); Similarity measure; Gradient descent; Pattern recognition (psychology); Markov random field; Computer vision; Image (mathematics); Artificial neural network; Image segmentation","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":[0.0007520214,0.0001238727,0.000171472,0.0002043963,0.0001518632,0.0001071124,0.0002586207,0.00007348122,0.0001228913],"category_scores_gemma":[0.0001930004,0.0001198062,0.00004592894,0.0002247442,0.0003211659,0.001618218,0.000003400715,0.0002496053,0.00001259198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004202839,"about_ca_system_score_gemma":0.00009787822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007132933,"about_ca_topic_score_gemma":0.000005539548,"domain_scores_codex":[0.9982126,0.0001360502,0.0005042875,0.0001983714,0.0007752745,0.0001733885],"domain_scores_gemma":[0.9987881,0.0004438495,0.0001823683,0.0002451845,0.0001356879,0.0002048013],"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":[0.00004035492,0.0002704028,0.00009891901,0.0001454857,0.00005516757,0.0000104283,0.002584766,0.0004634876,0.04068368,0.005931255,0.002351985,0.9473641],"study_design_scores_gemma":[0.001693194,0.0001643978,0.0002074662,0.0002543457,0.00003813194,0.0002498806,0.0003665217,0.7515552,0.2431108,0.001382738,0.0006427311,0.000334608],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004885619,0.000008559921,0.9934008,0.0006456946,0.0002775693,0.0001592767,0.000006642421,0.0001721744,0.0004436918],"genre_scores_gemma":[0.9243442,0.00002869796,0.07395649,0.001590506,0.00003384278,0.00001295139,0.000008212915,0.00000615231,0.00001898099],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9470295,"threshold_uncertainty_score":0.4885556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01106093204128132,"score_gpt":0.2783517643008688,"score_spread":0.2672908322595875,"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."}}