{"id":"W2779175107","doi":"10.1007/s11042-017-5484-1","title":"A target-oriented segmentation method for specific tissues in MRI images of the brain","year":2017,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Central City Brewers and Distillers (Canada)","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Image registration; Pattern recognition (psychology); Image segmentation; Image (mathematics)","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":[0.0003706883,0.00007540383,0.000118211,0.00004384263,0.0002361173,0.0001290962,0.0005632734,0.00003610968,0.00001158479],"category_scores_gemma":[0.0001642329,0.0000577549,0.00003400802,0.0001060284,0.0001430059,0.0003632111,0.0001535277,0.00006295133,0.000002345436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001600854,"about_ca_system_score_gemma":0.00002125669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003738196,"about_ca_topic_score_gemma":0.000006856912,"domain_scores_codex":[0.9992249,0.00004548301,0.0002310037,0.0002429656,0.0001417401,0.0001138926],"domain_scores_gemma":[0.9987788,0.0003400848,0.0002020433,0.0005619078,0.00007164953,0.00004551656],"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":[0.000003311309,0.00008371219,0.001106389,0.00002828391,0.000006289071,2.265751e-7,0.0007716576,0.000004729797,0.08473776,0.006693905,0.003776531,0.9027872],"study_design_scores_gemma":[0.001152885,0.00004216253,0.03631629,0.00004858535,0.000008081095,0.000002099766,0.0001933546,0.03506941,0.8878328,0.00919404,0.0299445,0.0001957432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002040269,0.00008977392,0.9943389,0.003817034,0.0000507481,0.001223154,0.0000502298,0.00003820318,0.0001879778],"genre_scores_gemma":[0.005326012,0.00005643508,0.9931589,0.0001684294,0.00004792615,0.00103261,0.00001944381,0.000005736126,0.0001844452],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9025915,"threshold_uncertainty_score":0.2355176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982090189379579,"score_gpt":0.3542899675578901,"score_spread":0.3244690656640943,"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."}}